Journal articles on the topic 'Plant maintenance Data processing'
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Clavijo, Nayher, Afrânio Melo, Maurício M. Câmara, Thiago Feital, Thiago K. Anzai, Fabio C. Diehl, Pedro H. Thompson, and José Carlos Pinto. "Development and Application of a Data-Driven System for Sensor Fault Diagnosis in an Oil Processing Plant." Processes 7, no. 7 (July 10, 2019): 436. http://dx.doi.org/10.3390/pr7070436.
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Predictive analytics is usually cited as one of the most important pillars of the digital transformation. For the oil industry, specifically, it is a common belief that issues like integrity and maintenance could benefit from predictive analytics. This paper presents the development and the application of a process-monitoring tool in a real process facility. The PMA (Predictive Maintenance Application) system is a data-driven application that uses a multivariate analysis in order to predict the system behavior. Results show that the use of a multivariate approach for process monitoring could not only detect an early failure at a metering system days before the operation crew, but could also successfully identify, among hundreds of variables, the root cause of the abnormal situation. By applying such an approach, a better performance of the monitored equipment is expected, decreasing its downtime.
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Mitrovic, Radivoje, Milan Tasic, Zarko Miskovic, Zoran Stamenic, and Dragan Jovanovic. "Data Acquisition and Automatisation of a Conveyor Idler Test Stand." Advanced Materials Research 633 (January 2013): 277–89. http://dx.doi.org/10.4028/www.scientific.net/amr.633.277.
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Precise determination of the time-to-failure for conveyor idlers allows the planning of regular conveyor system maintenance and well-timed replacement of worn conveyor garlands. Incidental delays in power plant coal supply leads to a significant reduction in overall thermal power plant efficiency. This paper describes a test stand for the laboratory testing of conveyor idlers, under the influence of different radial loads, which was developed by the Faculty of Mechanical Engineering at the University of Belgrade. Data acquisition and processing are also considered, which includes control, monitoring and automatisation of the test stand. Machine protective systems, which ensure a high level of machine safety, were specifically redesigned due to the importance of operator safety and health.
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Paillin, Daniel Bunga, and Yoyok Widiatmoko. "Rancangan Aplikasi Monitoring Online Untuk Meningkatkan Pemeliharaan Prediktif Pada PLTD." JURNAL SISTEM INFORMASI BISNIS 11, no. 1 (November 22, 2020): 9–17. http://dx.doi.org/10.21456/vol11iss1pp9-17.
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Evaluation of operating data is one of the most important things about preventing disruption to the diesel generating unit. At this time process of recording the condition of the power plant (log sheet) in PLTD Hative Kecil is still done manually so that the evaluation process of the data that has been taken is still not optimal, and the use of parameter data as a basis for improving engine performance has also not been maximized. The purpose of this study is to design a web-based plant operation monitoring application to optimize predictive maintenance, improve the efficiency and effectiveness of processing plant data operations and as a reference for maintenance planning and assist in making decisions so that Operational Excellence can be achieved. The results of the design can provide information on the condition of the engine in real-time that can be used for planning, controlling, and evaluating the predictive maintenance of diesel generators in PLTD Hative Kecil
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Iftikhar, Hamid, Eduardo Sarquis, and P. J. Costa Branco. "Why Can Simple Operation and Maintenance (O&M) Practices in Large-Scale Grid-Connected PV Power Plants Play a Key Role in Improving Its Energy Output?" Energies 14, no. 13 (June 24, 2021): 3798. http://dx.doi.org/10.3390/en14133798.
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Existing megawatt-scale photovoltaic (PV) power plant producers must understand that simple and low-cost Operation and Maintenance (O&M) practices, even executed by their own personal and supported by a comparison of field data with simulated ones, play a key role in improving the energy outputs of the plant. Based on a currently operating 18 MW PV plant located in an under-developing South-Asia country, we show in this paper that comparing real field data collected with simulated results allows a central vision concerning plant underperformance and valuable indications about the most important predictive maintenances actions for the plant in analysis. Simulations using the globally recognized software PVSyst were first performed to attest to the overall power plant performance. Then, its energy output was predicted using existing ground weather data located at the power plant. Compared with the actual plant’s annual energy output, it was found that it was underperforming by −4.13%, leading to a potential monetary loss of almost 175,000 (EUR)/year. Besides, an analysis of the O&M power plant reports was performed and compared to the best global practices. It was assessed that the tracker systems’ major issues are the forerunner of the most significant PV power plant underperformance. In addition, issues in inverters and combiner boxes were also reported, leading to internal shutdowns. In this case, predictive maintenance and automated plant diagnosis with a bottom-up approach using low-cost data acquisition and processing systems, starting from the strings level, were recommended.
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Arena, Simone, Giuseppe Manca, Stefano Murru, Pier Francesco Orrù, Roberta Perna, and Diego Reforgiato Reforgiato Recupero. "Data Science Application for Failure Data Management and Failure Prediction in the Oil and Gas Industry: A Case Study." Applied Sciences 12, no. 20 (October 20, 2022): 10617. http://dx.doi.org/10.3390/app122010617.
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In the industrial domain, maintenance is essential to guarantee the correct operations, availability, and efficiency of machinery and systems. With the advent of Industry 4.0, solutions based on machine learning can be used for the prediction of future failures by exploiting historical failure data. Most of the time, these historical data have been collected by companies without a specific structure, schema, or even best practices, resulting in a potential loss of knowledge. In this paper, we analyze the historical data on maintenance alerts of the components of a revamping topping plant (referred to as RT2) belonging to the SARAS group. This analysis is done in collaboration with the ITALTELECO company, a partner of SARAS, that provided the necessary data. The pre-processing methodology to clean and fill these data and extract features useful for a prediction task will be shown. More in detail, we show the process to fill missing fields of these data to provide (i) a category for each fault by using simple natural language processing techniques and performing a clustering, and (ii) a data structure that can enable machine learning models and statistical approaches to perform reliable failure predictions. The data domain in which this methodology is applied is oil and gas, but it may be generalized and reformulated in various industrial and/or academic fields. The ultimate goal of our work is to obtain a procedure that is simple and can be applied to provide strategic support for the definition of an adequate maintenance plan.
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Kim, Donghyun, Soonwook Kwon, Chung-Suk Cho, Borja García de Soto, and Daeyoon Moon. "Automatic Space Analysis Using Laser Scanning and a 3D Grid: Applications to Industrial Plant Facilities." Sustainability 12, no. 21 (October 31, 2020): 9087. http://dx.doi.org/10.3390/su12219087.
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While industrial plant projects are becoming bigger, and global attention to the plant as a construct is increasing, space arrangement in plant projects is inefficient because of the complex structure of required facilities (e.g., complex MEP (mechanical, electrical, and plumbing) installations, specialized tools, etc.,). Furthermore, problems during installation, operation, and maintenance stages caused by inconsistencies between floor plans and actual layout are on the rise. Although some of these conflicts can be addressed through clash detection using BIM (building information modeling), quality BIM models are scarce, especially for existing industrial plants. This study proposes a way to address the complexities caused by changes during plant construction and securing space for the installation of equipment during the construction and lifecycle of built facilities. 3D cloud point data of space and equipment were collected using 3D laser scanning to conduct space matching. In processing the space matching, data were simplified by applying the 3D grid and by comparing the data, easier identification of the space for target equipment was accomplished. This study also proposed a pre-processing method based on sub-sampling that optimizes the point cloud data and verifies the processing speed and accuracy. Lastly, it finds free space for various equipment layouts required in industrial plant projects by space analysis, proposed algorithms, and processes for obtaining the coordinates of valid space for equipment arrangement. The proposed method of this study is expected to help solve the problems derived from arrangement and installation of new equipment in a complex plant site.
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Holbert, Keith E., and Kang Lin. "Nuclear Power Plant Instrumentation Fault Detection Using Fuzzy Logic." Science and Technology of Nuclear Installations 2012 (2012): 1–11. http://dx.doi.org/10.1155/2012/421070.
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Economic constraints are driving the electric power industry to seek improved methods for monitoring, control, and diagnostics. To increase plant availability, various techniques have been implemented in industry to assess equipment condition to prevent system inoperability. The availability of a large number of measured signals and additional component information and the increasing number of signal processing options to analyze sampled data motivate the assimilation of such diverse information into a plantwide condition monitor. The use of fuzzy logic is described herein for the purpose of performing the decision making regarding the system status and the possible need for component maintenance. Fuzzy-logic-based diagnostic monitoring is applied to data acquired from instrumentation within operating facilities.
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Wang, Dingmei, Qiang Zhou, Yuze Du, and Haiying Dong. "Research on Distributed PV Monitoring System Based on Ubiquitous Power IOT Architecture." Journal of Physics: Conference Series 2166, no. 1 (January 1, 2022): 012025. http://dx.doi.org/10.1088/1742-6596/2166/1/012025.
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Abstract There are problems of low intelligent management and automatic maintenance of traditional distributed PV power plants. This paper designs a distributed PV monitoring system based on ubiquitous power IoT architecture. LoRa edge computing terminal, Storm data stream processing strategy and Spark in-memory programming model are introduced to meet the needs of distributed PV power plants for fast access and storage, analysis and mining of big data. This system improves the real-time and security of power plant data cloud-edge collaboration, and can efficiently perform status monitoring, maintenance management and data analysis of distributed PV power plants. It is of practical significance to improve the whole life cycle management of distributed photovoltaic power generation equipment and promote the transformation of operation and maintenance mode to automation, intelligence and intensification.
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Fukuda, Toshio, Hidemi Hosokai, Fumihito Arai, and Shusuke Mogi. "A Study on a 3-Dimensional Expert Vision System Using the Fiber Grating Method (The Neural Network Applications for Recognition of Plant Pipeline Direction)." Journal of Robotics and Mechatronics 3, no. 3 (June 20, 1991): 201–6. http://dx.doi.org/10.20965/jrm.1991.p0201.
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This paper describes a recognition method for plant pipeline direction, the rotational angle in the vertical plane and in depth. A neural network is applied using the vision method for the plant maintenance robot. For this purpose, a fiber grating method has been employed. The fiber grating is made of optical fiber through which laser beams form discrete dot patterns without any mechanical scanning method. Thus, the proposed method has the advantage of a shorter processing time compared to that of other methods. Although the proposed method does not provide a fine analysis of image data like the common scene analysis, it is suitable for obtaining rough information from the operating environment of robots. Experimental results are included to verify the proposed method.
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Hameed, Abdul, Syed Asif Raza, Qadeer Ahmed, Faisal Khan, and Salim Ahmed. "A decision support tool for bi-objective risk-based maintenance scheduling of an LNG gas sweetening unit." Journal of Quality in Maintenance Engineering 25, no. 1 (March 11, 2019): 65–89. http://dx.doi.org/10.1108/jqme-04-2017-0027.
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Purpose The purpose of this paper is to develop a decision support tool for risk-based maintenance scheduling for a large heavily equipped gas sweetening unit in a Liquefied Natural Gas (LNG) plant. Two conflicting objectives, i.e., total maintenance cost and the reliability, are considered in the tool. The tool is tested with the real plant data and suggests several Pareto-optimal schedules for a decision maker to choose from. The financial impacts are assessed. Design/methodology/approach A bi-objective scheduling optimization model is developed for maintenance scheduling using a risk-based framework. The model is developed integrating genetic algorithm and simulation-based optimization to find Pareto-optimal schedules. The model delivered true Pareto front optimal solutions for given plant-specific data. The two conflicting objectives: the minimization of total expenditures incurred on maintenance-related activities and improving the total reliability are considered. Findings For large and complex processing facilities such as LNG plant, a shutdown of facility generates a significant financial impact, resulting in millions of dollars in production loss. The developed risk-based equipment selection strategy helps to minimize such an event of production loss by generating a thorough maintenance strategy for inspection, repair, overhaul or replacement schedule of the unit without initiating the shutdown. The proposed model has been successfully applied to obtain an optimize maintenance schedule for a gas sweetening unit. Research limitations/implications A future work may consider the state-dependent models for various failure modes that will result in obtaining a better representation of the model. The proposed scheduling can further be extended to multi-criteria scheduling including availability, resource limitation and inflationary condition. A comparative analysis with other meta-heuristic techniques such as harmony search algorithm, tabu search, and simulated annealing will further help in confirming the schedule obtained from this application. Practical implications Maintenance scheduling using a conventional approach for special equipment generally does not consider the conflicting objectives. This research addresses this aspect using a bi-objective model. The usefulness of risk-based method is to assist in minimizing the financial and safety risk exposure to the operating companies, but some variation in results is expected due to varying risk matrix for different organizations. Social implications Managing two objectives, i.e., minimizing the cost of maintenance-related activities, while at the same time maximizing the overall reliability dramatically, helps in mitigating adverse safety and financial risk due to fires, explosions, fatality and excessive maintenance cost. Originality/value Research develops a decision support tool for managing conflicting objectives for an LNG process. This research highlights the impact of utilizing the simulation-based approach coupled with risk-based equipment selection for complex processing unit or plant maintenance scheduling optimization.
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Zhang, Weipeng, Bo Zhao, Liming Zhou, Jizhong Wang, Kang Niu, Fengzhu Wang, and Ruixue Wang. "Research on Comprehensive Operation and Maintenance Based on the Fault Diagnosis System of Combine Harvester." Agriculture 12, no. 6 (June 20, 2022): 893. http://dx.doi.org/10.3390/agriculture12060893.
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In view of the difficulty in diagnosing and discriminating fault conditions during the operation of combine harvesters, difficulty in real-time processing of health status, and low timeliness of fault processing, a comprehensive operation and maintenance platform for combine harvesters was developed in this study which realized the functions of data monitoring and the full operation and maintenance of a combine harvester. At the same time, through the comprehensive operation and maintenance platform, the harvester information was obtained in real-time, the diagnosis results were obtained, and the maintenance service was effectively carried out through the platform. The IPSO-SVM fault diagnosis algorithm was proposed, and the performance of the fault diagnosis of the combine harvester was verified by the simulation test. The experimental verification showed that the system met the requirements of remote monitoring of combine harvesters, and the prediction accuracy of this method was 97.96%. Compared with SVM (87.51%), GA-SVM (89.44%), and PSO-SVM (92.56%), this system had better generalization ability and effectively improved the management level of the comprehensive operation and maintenance of the combine harvester. A theoretical basis and technical reference will be provided for the follow-up research for the comprehensive operation and maintenance platform of the combine harvester in this paper.
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Kemerbayev, Nurgan T. "GEODETIC INFORMATION IN AUTOMATED SYSTEM OF TECHNICAL SUPPORT AND MAINTENANCE." Vestnik SSUGT (Siberian State University of Geosystems and Technologies) 25, no. 4 (2020): 27–36. http://dx.doi.org/10.33764/2411-1759-2020-25-4-27-36.
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Two years ago, at the Pavlodar oil chemistry refinery LLP, as part of the modernization of the production process, an automated system for maintenance and repair of equipment (PM) was introduced. This system, consisting of several subsystems, made it possible to monitor the workflow, monitor many production cycles, compile statistics on the performance of various production tasks, identify problematic zones, and also pre-predict the failure of a particular equipment, calculate problematic zones. However, the process of expanding the system of functional maintenance does not stand still. Currently, as part of the digitalization of the production process, the plant is implementing a project to create a 3D master plan for the plant. The article describes in detail the process of phased creation of a digital spatial model and its relationship with the automated control system for maintenance and repair of equipment. One of the main stages of creating a three-dimensional model is described in detail – the process of obtaining high-precision geodetic measurements to create a geodetic base, field work, and the creation of a plan-height justification. The methodology of laser scanning, the process of processing the obtained data, modeling and filling the digital model with attributive data is described. Today it is difficult to dispute the importance of introducing digital spatial modeling in production and the main role in the success of these innovative projects is played by correctly received, processed and implemented initial data in the model. Thus, geodetic support is a key step in the complex technological process of creating a digital spatial double or 3D model of the plant and its operation in the maintenance system.
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Wang, Hang, Min-jun Peng, Yong-kuo Liu, Shi-wen Liu, Ren-yi Xu, and Hanan Saeed. "Remaining Useful Life Prediction Techniques of Electric Valves for Nuclear Power Plants with Convolution Kernel and LSTM." Science and Technology of Nuclear Installations 2020 (August 28, 2020): 1–13. http://dx.doi.org/10.1155/2020/8349349.
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Electric valves have significant importance in industrial applications, especially in nuclear power plants. Keeping in view the quantity and criticality of valves in any plant, it is necessary to analyze the degradation of electric valves. However, it is difficult to inspect each valve in conventional maintenance. Keeping in view the quantity and criticality of valves in any plant, it is necessary to analyze the degradation of electric valves. Thus, there exists a genuine demand for remote sensing of a valve condition through nonintrusive methods as well as prediction of its remaining useful life (RUL). In this paper, typical aging modes have been summarized. The data for sensing valve conditions were gathered during aging experiments through acoustic emission sensors. During data processing, convolution kernel integrated with LSTM is utilized for feature extraction. Subsequently, LSTM which has an excellent ability in sequential analysis is used for predicting RUL. Experiments show that the proposed method could predict RUL more accurately compared to other typical machine learning and deep learning methods. This will further enhance maintenance efficiency of any plant.
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Ahsan, Q., R. I. Grosvenor, and P. W. Prickett. "Distributed On-Line System for Process Plant Monitoring." Proceedings of the Institution of Mechanical Engineers, Part E: Journal of Process Mechanical Engineering 220, no. 2 (May 1, 2006): 61–77. http://dx.doi.org/10.1243/09544089jpme53.
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This paper describes a methodology for monitoring industrial processes and plant that can be implemented cost-effectively within small-to-medium enterprises. The methodology is based on a network of 8-bit microcontrollers that communicate with each other on a controller area network bus. Ethernet connectivity is provided so that remote users can access the system on the internet. The software models developed for data acquisition nodes and the design of remote user interfaces and supervisory nodes are also explained. The system is aimed at providing specific maintenance guidance and fault identification, rather than gathering data for off-line analysis. Overly complicated processing is avoided to make real-time implementation possible, using 8-bit microcontrollers. The methodology emphasizes the use of process controller signals for fault detection and sensor signals for fault isolation. The suitability of the methodology is explored by acquiring signals from a laboratory-based process rig. Suitable monitoring techniques for the system in time and frequency domains are also discussed.
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M., Kalwang, M., Kulla D. M., Agontu J. A., and Mafai, G. S. "An Assessment of the Impact and Benefits of Total Productive Maintenance (TPM) On Manufacturing Profitability: A Case Study of a Food Processing Industry in Nigeria." International Journal of Engineering and Computer Science 9, no. 09 (September 10, 2020): 25175–88. http://dx.doi.org/10.18535/ijecs/v9i09.4511.
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This study was carried out to assess the impact and benefits of Total Productive Maintenance on manufacturing profitability awareness for Grand Cereals Ltd., Jos-Nigeria). The research objective is to assess the impact and benefits of TPM on manufacturing profitability of the company. Data relevant to the study were collected using designed questionnaire, structured interviews, direct observations and company records. The Statistical Package for Social Sciences (SPSS) and Microsoft Excel were used in analysing the data obtained. The results show that the frequency of machine and equipment breakdowns is 58% often and 42% rarely. The research also reviewed that 84% of machine operators were not involved in maintenance activities with only 16% involvement. As regards to the effectiveness of the maintenance technique(s) used, 76% was rated good while 24% was rated fair. Cereal Mill Overall Plant Effectiveness (OPE) was obtained at 38% which was below the world class standard by almost 50%. Equipment downtime was a major cause of plant underutilization with 91% caused by shortage of critical spare parts and 9% shortage of raw materials. On TPM awareness, 80% of the employees have been aware of the TPM concept while 98% indicated the concept of TPM would help improve the current maintenance system and 2% were not sure.20% of the employees were not aware of TPM while 80% of employees indicated that the company does not implement TPM, with 20% not sure that the TPM concept can help improve the current maintenance system. Based on these results, knowledge and information sharing, operator involvement, engagement of all employees from top management to lower level and training should be considered. The effective implementation of TPM model could increase the competition of the company in the dynamic business environment. Thus, adoption and effective implementation of TPM is recommended for the company towards achieving improvement in its manufacturing profitability.
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Hasibuan, Chalis Fajri. "The Instensity Measurement And Noise Mapping in Fatty Acid Plant Area At PT. XYZ." Simetrikal: Journal of Engineering and Technology 2, no. 1 (February 29, 2020): 20–27. http://dx.doi.org/10.32734/jet.v2i1.3556.
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PT. XYZ is a factory engaged in processing of palm oil derivatives in producing a fatty acid. The machines used in the processing process at PT. Permata Hijau Palm Oleo KIM II Mabar generate the noise. This research aimed to find out the existing noise level and noise mapping, also the proposal of noise control on the production floor. Then, the data collection method conducted through observation using the equivalent noise level (Leq) method and noise mapping was through surfer 14. The data collection conducted in 16 points on the production floor. Furthermore, the result and noise distribution pattern showed that the high noise level was in several points, those were point 5 (85.77); point 6 (86.82); point 7 (86.33), point 8 (88.18); point 10 (86.96); point 13 (86.85); point 14 (87.67). The allowed threshold value refers to the Decree of the Minister of Manpower and Transmigration No.Per.13/MEN/X/2011 is 85 dB. Thus, the company needs to perform noise control such as barrier usage, regularly and scheduled machine maintenance to prevent and decrease the effect of the noise
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Eerikäinen, S., H. Haimi, A. Mikola, and R. Vahala. "Data analytics in control and operation of municipal wastewater treatment plants: qualitative analysis of needs and barriers." Water Science and Technology 82, no. 12 (July 6, 2020): 2681–90. http://dx.doi.org/10.2166/wst.2020.311.
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Abstract This study aims to identify barriers and needs for the application of data analytics in municipal wastewater treatment. The study was conducted through a series of interviews with stakeholders involved in instrumentation, control, and automation of wastewater treatment plants. Opportunities and limitations observed by different stakeholders were assessed with a thematic analysis. Thematic analysis enabled a broader consideration of social and organizational aspects related to process control, operation, and maintenance. Identified key barriers for applying data analytics included laborious instrumentation maintenance, unstable control loops, and deficient customization of digital tools for users at wastewater treatment plants. Development needs include easier data processing tools, quality assurance of instrumentation, and controller tuning. Results indicate that the perceived potential of data analytics is highly dependent on the performance of underlying physical and digital systems, as well as the control strategies and operating environment of the plant. Despite the barriers, users and developers see many potential applications for data analytics and expect them to have a central role in the control and operation of wastewater treatment plants in the future.
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Johnson, John E., and Donald E. Wasserman. "Ergonomics & Vibration in Powered Hand Tools Used in Meat Processing." Journal of Low Frequency Noise, Vibration and Active Control 19, no. 1 (March 2000): 1–15. http://dx.doi.org/10.1260/0263092001492750.
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The purpose of this study was to determine the triaxial acceleration levels of a group of powered circular knives extensively used in the meat processing industry. These tools were tested in real time under actual work conditions while the operators used the tools in 23 different applications. The length of the study was 11 months, encompassing 685 tools, at 13 plant sites using 5 different diameters of circular tools. All vibration tool measurements were recorded from three triaxially mounted individually calibrated, PCB accelerometers. Customized software programs allowed a Gateway 2000 SOLO computer to cycle, collect, and record vibration data for a minimum of a one minute time frame. The study results showed that neither the ANSI S3.34 nor the ACGIH hand-arm vibration standards were exceeded for an 8–hour workday. The median acceleration levels for the five different sized diameter tools did not significantly differ. The operating levels increased only slightly from a new tool that has been determined to be operating at maximum efficiency. Examining the different tool applications, operators, plant locations and tool conditions resulted in concluding that for this type of tool the most important factor contributing to the acceleration intensity levels was tool maintenance and the conditions of its parts. Tools with higher vibration acceleration levels were disassembled and found to have at least one part severely worn or damaged. After the part(s) were replaced, the tool was re-tested under the same work conditions, the vibration acceleration levels on average decreased by 16%. As part of an overall effort, operator exposure to vibration generated by these powered circular knives can be significantly reduced and controlled by proper tool care and maintenance.
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Zulkafli, Nur Izyan, and Reduan Mat Dan. "Investigation of maintenance performance for a gasification process unit using Weibull analysis." Journal of Quality in Maintenance Engineering 22, no. 3 (August 8, 2016): 252–63. http://dx.doi.org/10.1108/jqme-08-2015-0039.
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Purpose – The purpose of this paper is to investigate maintenance performance of a gasification process unit by identifying reliability, failure and hazard rate. The prediction on the number of preventive maintenance (PM) activities and size of labour are being analysed. Design/methodology/approach – The study collects maintenance data for 4,000 hours operation to perform Weibull analysis in order to determine two key factors which are beta shape factor, β and characteristic life, η. Findings – The results for estimation of failure, reliability and hazard rate show that the pump was most likely contributed to the biggest failure. On the other hand, reaction chamber was able to maintain the longest operation among other components. It is estimated that the total PM activities should be within 20-50 activities per month for whole processing plant. Meanwhile, the estimation of size of labour should be within the range of 60-130 numbers of workers per month for all components. Originality/value – The method of Weibull analysis for investigating current maintenance performance has been analysed using real case study data. The data collection is obtained from a typical gasification process unit.
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Graefe, Goetz, Anisoara Nica, Knut Stolze, Thomas Neumann, Todd Eavis, Ilia Petrov, Elaheh Pourabbas, and David Fekete. "Elasticity in Cloud Databases and Their Query Processing." International Journal of Data Warehousing and Mining 9, no. 2 (April 2013): 1–20. http://dx.doi.org/10.4018/jdwm.2013040101.
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A central promise of cloud services is elastic, on-demand provisioning. The provisioning of data on temporarily available nodes is what makes elastic database services a hard problem. The essential task that enables elastic data services is bringing a node and its data up-to-date. Strategies for high availability do not satisfy the need in this context because they bring nodes online and up-to-date by repeating history, e.g., by log shipping. Nodes must become up-to-date and useful for query processing incrementally by key range. What is wanted is a technique such that in a newly added node, during each short period of time, an additional small key range becomes up-to-date, until eventually the entire dataset becomes up-to-date and useful for query processing, with overall update performance comparable to a traditional high-availability strategy that carries the entire dataset forward without regard to key ranges. Even without the entire dataset being available, the node is productive and participates in query processing tasks. The authors’ proposed solution relies on techniques from partitioned B-trees, adaptive merging, deferred maintenance of secondary indexes and of materialized views, and query optimization using materialized views. The paper introduces a family of maintenance strategies for temporarily available copies, the space of possible query execution plans and their cost functions, as well as appropriate query optimization techniques.
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Rusa, Ionel, Cornel Marin, and Marius Baidoc. "Experimental Results of Vibroacoustic Diagnosis Performed with Vibro-Expert System." Scientific Bulletin of Valahia University - Materials and Mechanics 16, no. 14 (April 1, 2018): 38–51. http://dx.doi.org/10.1515/bsmm-2018-0007.
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Abstract The implementation of proactive maintenance is a necessity required by the development of modern technologies for the monitoring and exploitation of energy facilities and equipment. An important advantage of proactive maintenance is to permanently monitor the technical condition of the plant and equipment by vibration measurements and in the correct diagnosis in order to reasonably plan the required repairs. Turbo-aggregates are autonomous complex installations for producing electricity in refineries that operate in high power and high-speed modes. To monitor and control turbine vibrations, vibration sensors (uniaxial, biaxial and triaxial accelerometers) and proximity sensors (for relative displacements and lasers) are columned on bearing housings that transmit signals to data acquisition and processing systems as well is the Vibro-Expret diagnosis system presented in this paper.
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Erdei, Timotei, Zsolt Molnár, Nwachukwu C. Obinna, and Géza Husi. "A Novel Design of an Augmented Reality Based Navigation System & its Industrial Applications." ACTA IMEKO 7, no. 1 (April 1, 2018): 57. http://dx.doi.org/10.21014/acta_imeko.v7i1.528.
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<p>Maintenance activities are integral within an industrial setting. The efficiency of these activities is associated with the total productivity of an industrial process/machine. A highly efficient maintenance policy/strategy usually results in relatively high levels of plant productivity. AR and VR may be incorporated into a maintenance strategy. AR and VR technology would enhance maintenance activities, and facilitate somewhat complex tasks.</p><p>A maintenance strategy includes a range of activities and may be categorized into administrative, technical and management processes. AR technology contains digital data, as well as other technical details, and is able to provide information about industrial machinery-equipment, without the need for equipment disassembling.</p><p>In this regard, we employed AR technology in developing a unique navigation system to replace/reduce the installation costs of traditional AGV navigation systems. The proposed AR system consists of a camera, which observes the QR-Code/Markers, and a processing unit.</p><p>Augmented Reality enables visualisation of any data and information, as well as control of a running process. It means it is possible to read various data within any equipment, during its operation and in real-time. This facilitates analysis of “black box” systems.</p>
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Owusu-Mensah, Daniel, Ren Naifei, Lydia Brako, Priscilla Boateng, and Williams Kweku Darkwah. "Analysis of Production System Management of Ghana’s Food and Beverage Industry: Empirical evidence from Spare Parts Inventory Control, Production Quality and Maintenance Modeling." Journal of Food Industry 4, no. 1 (April 13, 2020): 1. http://dx.doi.org/10.5296/jfi.v4i1.16511.
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The production systems of various industries have increasingly become a strategic business management function that drives differentiation and competitive edge. Production plant maintenance is therefore an integral part of an entire production system management function, which should encapsulate spare part inventory control systems, total quality assurance, improvement measures and cost control measures. This project sought to understand the production process of firms in Ghana’s food and beverage industry, assess the spare part inventory management practices, production quality control practices and maintenance management strategies implemented in order to recommend a maintenance optimization modeling system towards ensuring an optimal operation cost and production quality. Using both the inductive and deductive research approaches, the research project was exploratory and explanatory in nature. Three major players in Ghana’s food and beverage industry: GIHOC-DCL (Ghana Industrial Holding Corporation Distillery Limited), Heathy-Life Ghana Limited (Healthy-Life) and Parlays Ghana Limited (Parlays) were purposively sampled as industrial case studies to generate data from documentary secondary data and survey based sources. The functionality tools available in MATLAB/Simulink software assisted in formulating the maintenance optimization problem, modeling, analyzing and deriving solutions. The study found that the food and beverage processing business in Ghana has a positive correlation with the main agricultural produce of the country and involves a series of activities including processing and preservation of meat, fish, fruit, vegetables, starch products, bread and sugar. Spare part inventory management and quality control tools mainly applied include Statistical Control Charts and Check Sheet rather than optimization models and software.
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Aidin, Alfillah, Munirwan Zani, and Lukman Yunus. "The Technical Feasibility Analysis of Sorghum Farming In Lamunde Village Tinondo District East Kolaka Regency." Jurnal Ilmiah Membangun Desa dan Pertanian 7, no. 2 (April 30, 2022): 50. http://dx.doi.org/10.37149/jimdp.v7i2.24256.
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The research objective is to analyze the technical feasibility of farming sorghum in Lamunde Village District Tinondo Regency East Kolaka. The research was carried out in the Village Lamunde District Tinondo Regency East Kolaka in July 2021. The research population, namely sorghum farmers in Lamunde Village, amounted to 25 people, while the research sample was determined using the census method. This study uses primary data and secondary data. Data collection techniques using direct interview and study method literature. Research variables include seeding, processing, land, planting, fertilizing, maintenance, pests, plant disease, and harvesting. The research was analyzed using descriptive analysis based on the scoring results. Research result shows that sorghum farming in the village of. Lamunde, Tinondo District, East Kolaka Regency is technically feasible, namely land management activities, planting activities, fertilizing activities, maintenance activities, pest control activities, and disease crops and harvesting activities included in the high category. Meanwhile, most of the seeding activities are in the medium category.
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Trần, Ngọc Trung, Hùng Trường Triệu, Vũ Tùng Trần, Hữu Hải Ngô, and Quang Khoa Đào. "An overview of the application of machine learning in predictive maintenance." Petrovietnam Journal 10 (November 30, 2021): 47–61. http://dx.doi.org/10.47800/pvj.2021.10-05.
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With the rise of industrial artificial intelligence (AI), smart sensing, and the Internet of Things (IoT), companies are learning how to use their data not only for analysing the past but also for predicting the future. Maintenance is a crucial area that can drive significant cost savings and production value around the world.
Predictive maintenance (PdM) is a technique that collects, cleans, analyses, and utilises data from various manufacturing and sensing sources like machines usage, operating conditions, and equipment feedback. It applies advanced algorithms to the data, automatically compares the fed data and the information from previous cases to anticipate or predict equipment failure before it happens, thus helping optimise equipment utilisation and maintenance strategies, improve performance and productivity, and extend equipment life. Robust PdM tools enable organisations to leverage and maximise the value of their existing data to stay ahead of potential breakdowns or disruptions in services, and address them proactively instead of reacting to issues as they arise. Therefore, it has attracted more and more attention of specialists in recent years.
This paper provides a comprehensive review of the recent advancements of machine learning (ML) techniques widely applied to PdM by classifying the research according to the ML algorithms, machinery and equipment used in data acquisition. Important contributions of the researchers are highlighted, leading to some guidelines and foundation for further studies. Currently, BIENDONG POC is running some pilot PdM projects for critical equipment in Hai Thach - Moc Tinh gas processing plant.
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Maduekwe, Victor Chidiebere, and Sunday Ayoola Oke. "Novel Taguchi scheme–based DEMATEL methods and DEMATEL method for the principal performance indicators of maintenance in a food processing industry." International Journal of Intelligent Computing and Cybernetics 14, no. 3 (April 29, 2021): 363–97. http://dx.doi.org/10.1108/ijicc-12-2020-0209.
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PurposeKey performance indicators (KPIs) of maintenance systems serve as benchmarks to workers and organizations to compare their goals for decision-making purposes. Unfortunately, the effects of one KPI on the other are least known, restraining decisions on prioritization of KPIs. This article examines and prioritizes the KPIs of the maintenance system in a food processing industry using the novel Taguchi (T) scheme-decision-making trial and evaluation laboratory (DEMATEL) method, Taguchi–Pareto (TP) scheme–DEMATEL method and the DEMATEL method.Design/methodology/approachThe causal association of maintenance process parameters (frequency of failure, downtime, MTTR, MTBF, availability and MTTF) was studied. Besides, the optimized maintenance parameters were infused into the DEMATEL method that translates the optimized values into cause and effect responses and keeping in view the result of analysis. Data collection was done from a food processing plant in Nigeria.FindingsThe results indicated that downtime and availability have the most causal effects on other criteria when DEMATEL and T-DEMATEL methods were respectively applied to the problem. Furthermore, the frequency of failure is mostly affected by other criteria in the key performance indication selection using the two methods. The combined Taguchi scheme and DEMATEL method is appropriate to optimize and establish the causal relationships of factors.Originality/valueHardly any studies have reported the joint optimization and causal relationship of maintenance system parameters. However, the current study achieves this goal using the T-DEMATEL, TP-DEMATEL and DEMATEL methods for the first time. The applied methods effectively ease decisions on prioritization of KPIs for enhancement.
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Sinaga, Benita Septiana, Zulham Effendi, and Giyanto . "ANALISA REALIBILITY DAN MAINTAINABILITY PADA MESIN SCREW PRESS DENGAN MENGGUNAKAN METODE DISTRIBUSI NORMAL DI PABRIK KELAPA SAWIT." Jurnal Agro Fabrica 1, no. 1 (July 2, 2019): 9–13. http://dx.doi.org/10.47199/jaf.v1i1.22.
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The cessation of the processing process at the palm oil mill will cause losses to the company, which can result in low productivity of the plant. Damage to equipment at the palm oil mill requires more costs to repair the damage. For this reason, good maintenance is needed for each equipment used in the processing process using the normal distribution method. In this study the tool used was Screw Press. The purpose of this study was to determine the value of Realibility and Maintainabiity by using a normal distribution and to determine the cause of damage to the Screw Press machine. This research stage was carried out by taking data on damage and repair of Screw Press in April-June 2017 and processing data using normal distribution. The data needed in normal distribution is the value of Realibility, Maintainability, and standard devisia. By doing this data processing, the Screw Press machine can operate well with reliability value in April-June 2017 on screw press 1 machines at 98%, 98%, 99% and maintainability value of 21.78 hours, 18.8 hours, 19 , 18 hours. Whereas in screw press machine 2 in April - June 2017 the reliability value is 99%, 98%, 95% and the Maintainability value is 19.9 hours, 18.68 hours and 18 hours.
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Poku-Agyemang, Kwasi Nyarko, and Alexander Reiterer. "3D Reconstruction from 2D Plans Exemplified by Bridge Structures." Remote Sensing 15, no. 3 (January 23, 2023): 677. http://dx.doi.org/10.3390/rs15030677.
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Due to increasing traffic on roads and railways, the maintenance of bridges is becoming more and more important. Building Information Modelling (BIM) provides the perfect basis to efficiently plan these maintenance activities. However, for historic bridges, which moreover require intensive maintenance, there is no BIM available. The demand to digitize these bridges is correspondingly high. Further, to the already existing measurement methods (laser scanning, photogrammetry, etc.), a novel workflow for the digitalization of bridges from 2D plans is presented. Based on image processing for corner detection, 3D point cloud reconstruction of parts and fusion of reconstructed parts can be used create a 3D object to scale. The point cloud can serve as a supplement to as-built laser scanning or camera data. The presented method was evaluated based on three bridges over the Dreisam river in Freiburg, Germany.
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Prasetyo, Yudha, and Japri Lukman. "PENERAPAN RELIABILITY CENTERED MAINTENANCE PADA PERALATAN SHIP UNLOADER PLTU TENAYAN 2 X 110 MW." Jurnal Teknik Industri Terintegrasi 3, no. 2 (January 4, 2021): 6–13. http://dx.doi.org/10.31004/jutin.v3i2.1346.
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Abstract
The Tenayan Steam Power Plant (PLTU) is a fossil fuel power plant that is coal with a low rank coal type. To be able to meet the needs of electricity on the island of Sumatra, especially the Riau and Riau Islands, the Tenayan power plant must maintain coal needs within a safe range of 15-25 days of operation. Coal needs may not experience a decrease in productivity due to equipment damage. The obstacle that arises is the frequent occurrence of unexpected downtime which results in disrupted coal production so that the supply of coal is insufficient for the unit to operate so that the unit must be derating. This is caused by damage to one of the coal dismantling equipment, the ship unloader. This study aims to provide equipment maintenance and optimal inspection time intervals on ship unloader equipment. The research method used is Reliability Centered Maintenance (RCM) with quantitative analysis. After analysis, it was found that the critical component of the Tenayan PLTU ship unloader was the chain bucket. This is obtained based on the component with the most damage frequency, which is 16 damage over a 3-year period. From the results of data processing, the MTTR value is 62.72 hours and MTTF is 211.02 hours, while the optimal inspection time interval is 6 days, so it is recommended to do reactive, inspection, and preventive maintenance activities on the chain bucket periodically every 6 days in order to increase the reliability of the chain bucket.
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Tuncev, Denis, Vitaliy Har'kov, and Maksim Kuznecov. "PROCESSING SUNFLOWER HUSK INTO HIGH STRENGTH COAL BRIQUETTES." Vestnik of Kazan State Agrarian University 14, no. 4 (April 12, 2020): 86–90. http://dx.doi.org/10.12737/2073-0462-2020-86-90.
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An urgent problem of the modern agro-industrial complex is the low efficiency of the use of secondary raw materials. On the basis of Rosstat data, in 2018 almost 13 million tons of sunflower seeds were collected in the country, which led to the accumulation of up to 1.8-3.2 million tons of husk, which is the ballast component of the technology for producing sunflower oil. Oil refineries continuously bear the costs of storage, safety, and the removal and disposal of such waste in landfills. Sunflower husk has a high calorific value, so thermochemical processing methods allow for the integrated processing of unclaimed raw materials of plant origin with moderate capital costs in energy and various chemical products. An experimental laboratory setup for conductive pyrolysis of plant materials has been developed to produce high-strength coal briquettes. The advantages of conductive heat feed pyrolysis reactors are simplicity of design and ease of maintenance. The results of a pilot study of the conductive pyrolysis process of sunflower husk showed that the obtained coal briquettes have a low ash content (6.2%), their maximum yield (29%) was achieved at a pressing pressure of 25 kg/cm2, and the maximum density of the samples was 1139 kg/cm3 (pressing pressure 153 kg/cm2). It was found that an increase in pressing pressure from 20 to 150 kg/cm2leads to an increase in the density of coal briquettes by 16%, and the maximum value of compression pressure is 566 kg/cm2. Also, at a compaction pressure of more than 50 kg/cm2, the impact resistance index during discharge reaches 100%. The developed technology provides a large mass yield of coal briquettes with high quality characteristics, which indicates the promise of utilizing sunflower husk by conductive pyrolysis in the fuel feed for the metallurgical industry.
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de Carvalho Chrysostomo, Giovanni Gravito, Marco Vinicius Bhering de Aguiar Vallim, Leilton Santos da Silva, Leandro A. Silva, and Arnaldo Rabello de Aguiar Vallim Filho. "A Framework for Big Data Analytical Process and Mapping—BAProM: Description of an Application in an Industrial Environment." Energies 13, no. 22 (November 18, 2020): 6014. http://dx.doi.org/10.3390/en13226014.
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This paper presents an application of a framework for Big Data Analytical Process and Mapping—BAProM—consisting of four modules: Process Mapping, Data Management, Data Analysis, and Predictive Modeling. The framework was conceived as a decision support tool for industrial business, encompassing the whole big data analytical process. The first module incorporates in big data analytical a mapping of processes and variables, which is not common in such processes. This is a proposal that proved to be adequate in the practical application that was developed. Next, an analytical “workbench” was implemented for data management and exploratory analysis (Modules 2 and 3) and, finally, in Module 4, the implementation of artificial intelligence algorithm support predictive processes. The modules are adaptable to different types of industry and problems and can be applied independently. The paper presents a real-world application seeking as final objective the implementation of a predictive maintenance decision support tool in a hydroelectric power plant. The process mapping in the plant identified four subsystems and 100 variables. With the support of the analytical workbench, all variables have been properly analyzed. All underwent a cleaning process and many had to be transformed, before being subjected to exploratory analysis. A predictive model, based on a decision tree (DT), was implemented for predictive maintenance of equipment, identifying critical variables that define the imminence of an equipment failure. This DT model was combined with a time series forecasting model, based on artificial neural networks, to project those critical variables for a future time. The real-world application showed the practical feasibility of the framework, particularly the effectiveness of the analytical workbench, for pre-processing and exploratory analysis, as well as the combined predictive model, proving effectiveness by providing information on future events leading to equipment failures.
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Darno. "PERENCANAAN PEMELIHARAAN MOTOR DIESEL DENGAN METODE RCM (RELIABILITY CENTERED MAINTENANCE) PADA PLTG TELUK LEMBU PEKANBARU." Jurnal Surya Teknika 7, no. 1 (December 13, 2020): 77–85. http://dx.doi.org/10.37859/jst.v7i1.2353.
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Teluk Lembu Gas Power Plant (PLTG) is one type of power plant that uses the power of burning fuel and high pressure air. To be able to meet the needs of electrical energy on the island of Sumatra, especially in the Riau region,electricity production must not experience a decrease in productivity due to equipment damage. Obstacles that arise are frequent downtime unexpectedresulting in the production of electricity is interrupted or even to cause derating caused by damage to the operational when the engine is running. This study aims to provide recommendations for equipment maintenance andtime intervals optimal inspectionin the generator system. The research method used is Reliability Centered Maintenance (RCM) withanalysis quantitative. Where are the stages of implementing the RCM method, namely determining thecomponents criticalin the PLTG generator engine, determining thevalues, determining the Mean Time to Failure (MTTF) and Mean Time to Repair (MTTR)time interval optimal maintenance, and making a table of causes of damage to thecomponents generator engine. After analyzing it, it was found that thecomponent criticalof PLTG Teluk Lembu is the Diesel Motor. This is obtained based on the component with the highest frequency of damage, namely 13 damage during a period of 1 year. From the results of data processing, it is obtained that the MTTF value is 0.92441 hours and the MTTR value is 0.89014 hours. Theinspection time interval optimalis 25 days, so it is advisable to carry out Corrective and Preventive maintenance activities for Diesel Motorcycles periodically every 25 days (PM 25D) in order to increase the reliability of the Diesel Motor
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M. A., Burhanuddin, Sami M. Halawani, and A. R. Ahmad. "A Costing Analysis for Decision Making Grid Model in Failure-Based Maintenance." Advances in Decision Sciences 2011 (August 1, 2011): 1–15. http://dx.doi.org/10.1155/2011/205039.
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Background. In current economic downturn, industries have to set good control on production cost, to maintain their profit margin. Maintenance department as an imperative unit in industries should attain all maintenance data, process information instantaneously, and subsequently transform it into a useful decision. Then act on the alternative to reduce production cost. Decision Making Grid model is used to identify strategies for maintenance decision. However, the model has limitation as it consider two factors only, that is, downtime and frequency of failures. We consider third factor, cost, in this study for failure-based maintenance. The objective of this paper is to introduce the formulae to estimate maintenance cost. Methods. Fish bone analysis conducted with Ishikawa model and Decision Making Grid methods are used in this study to reveal some underlying risk factors that delay failure-based maintenance. The goal of the study is to estimate the risk factor that is, repair cost to fit in the Decision Making Grid model. Decision Making grid model consider two variables, frequency of failure and downtime in the analysis. This paper introduces third variable, repair cost for Decision Making Grid model. This approaches give better result to categorize the machines, reduce cost, and boost the earning for the manufacturing plant. Results. We collected data from one of the food processing factories in Malaysia. From our empirical result, Machine C, Machine D, Machine F, and Machine I must be in the Decision Making Grid model even though their frequency of failures and downtime are less than Machine B and Machine N, based on the costing analysis. The case study and experimental results show that the cost analysis in Decision Making Grid model gives more promising strategies in failure-based maintenance. Conclusions. The improvement of Decision Making Grid model for decision analysis with costing analysis is our contribution in this paper for computerized maintenance management system.
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Engdahl, Chris, and David Hattrick. "Integrated Asset Performance Management: a true, holistic view of asset health and risk." APPEA Journal 62, no. 2 (May 13, 2022): S107—S111. http://dx.doi.org/10.1071/aj21155.
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The benefits of optimised operational asset effectiveness are compelling; however, deployment of Asset Performance Management (APM) platforms has often been handicapped by organisational demarcation, localised APM ‘heroes’ driving for success against the headwind of a reactive business culture and ongoing erosion of corporate operational asset knowledge. Increasingly, APM initiatives are also overrun by the increasing scope of corporate ‘Big Data’ projects, whereby data volume rather than informational context inadvertently becomes the objective. Execution of maintenance strategies is one of the single biggest influencers on asset performance, and optimising that program requires accurate insight into the constantly changing operating context, environment and asset health. Asset maintenance plans need to change dynamically, ideally updating in real time. Integrated APM harmonises the asset management functions, which may otherwise sit in independent silos, including reliability strategy, asset health, root cause analysis and defect elimination. Integration ensures consistency and standardisation in policies and strategies, accelerating improvement and enabling corporate learning and best practice to propagate. This paper illustrates how edge data processing, Industrial Internet of Things (IIOT) connectivity and cloud computing come together to deliver real outcomes for innovative enterprises. Plant-wide data systems that link failure-mode specific data sets directly with asset strategy, lead to faster root cause determination, work planning and execution. Practical examples of where this crucial step of parsing machinery information according to operating context and known failure modes drives tangible benefits within the integrated APM process are discussed. Well-considered technologies, policies and practices will deliver predictable, optimal asset performance and the ensuing business benefits.
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Sholomitsky, Andrei A., Nurgan T. Kemerbaev, Sergei G. Mogilny, and Sergei N. Tsarenko. "DEFORMATION AND STRESS ANALYSIS OF VERTICAL STEEL SHELL OF TANKS BASED ON LASER SCANNING DATA." Vestnik SSUGT (Siberian State University of Geosystems and Technologies) 27, no. 2 (2022): 86–101. http://dx.doi.org/10.33764/2411-1759-2022-27-2-86-101.
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The article considers a new technology of measurements of vertical steel tanks (ground-based laser scanning), which delivers a huge volume of unstructured information. There has been carried out a review of existing methods for solving tasks of tank walls' defomations determinations on laser reflec-tion point cloud. It is noted that the accuracy of laser scanning and scans' stitching is sufficient for tank walls' defomations determinations. The existing methods are implemented basically through building irregular triangular surfaces of tank shell on which then surface profiles are built manually or semi-automatically and the defomations are determined and compared to tolerance values. The au-thors suggest a method and algorithm for processing laser reflection point cloud, which by means of several infiltrations by height, radius tolerance and inclinations and interpolation by collocation meth-od is drawn to a rectangular topologically equivalent grid. These allow by several orders to decrease the information volume stored in the database, and regular grid model structure allows to apply numer-ical calculation method for calculating strains occurring in the tank walls. The strains are determined on semi-momentless theory for cylinder shell calculation. The method and algorithm are implemented in geodetic subsystem of automatic system of technical maintenance and repairs of equipment on the oil processing plant. The suggested method will allow in the future to move from normative assess-ment of technical tank state to the assessment on actual strains, which gives objective assessment of tank state for making substantiated decision on the prolongation of its exploitation, current mainte-nance and repair.
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Danubianu, Mirela, Dragos Mircea Danubianu, Cristian Teodorescu, and Lucian Constantin. "Data-Mining – A Valuable Managerial Tool for Improving Power Plants Efficiency." Present Environment and Sustainable Development 8, no. 1 (May 1, 2014): 205–16. http://dx.doi.org/10.2478/pesd-2014-0018.
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Abstract Energy and environment are top priorities for the EU’s Europe 2020 Strategy. Both fields imply complex approaches and consistent investment. The paper presents an alternative to large investments to improve the efficiencies of existing (outdated) power installations: namely the use of data-mining techniques for analysing existing operational data. Data-mining is based upon exhaustive analysis of operational records, inferring high-value information by simply processing records with advanced mathematical / statistical tools. Results can be: assessment of the consistency of measurements, identification of new hardware needed for improving the quality of data, deducing the most efficient level for operation (internal benchmarking), correlation of consumptions with power/ heat production, of technical parameters with environmental impact, scheduling the optimal maintenance time, fuel stock optimization, simulating scenarios for equipment operation, anticipating periods of maximal stress of equipment, identification of medium and long term trends, planning and decision support for new investment, etc. The paper presents a data mining process carried out at the TERMICA - Suceava power plant. The analysis calls for a multidisciplinary approach, a complex team (experts in power&heat production, mechanics, environmental protection, economists, and last but not least IT experts) and can be carried out with lower expenses than an investment in new equipment. Involvement of top management of the company is essential, being the driving force and motivation source for the data-mining team. The approach presented is self learning as once established, the data-mining analytical, modelling and simulation procedures and associated parameter databases can adjust themselves by absorbing and processing new relevant information and can be used on a long term basis for monitoring the performance of the installation, certifying the soundness of managerial measures taken and suggesting further adjustments
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Toledo, Carlos, Lucía Serrano, Jose Abad, Antonio Lampitelli, and Antonio Urbina. "Measurement of Thermal and Electrical Parameters in Photovoltaic Systems for Predictive and Cross-Correlated Monitorization." Energies 12, no. 4 (February 19, 2019): 668. http://dx.doi.org/10.3390/en12040668.
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Photovoltaic electricity generation is growing at an almost exponential rate worldwide, reaching 400 GWp of installed capacity in 2018. Different types of installations, ranging from small building integrated systems to large plants, require different maintenance strategies, including strategies for monitorization and data processing. In this article, we present three case studies at different scales (from hundreds of Wp to a 2.1 MWp plant), where automated parameter monitorization and data analysis has been carried out, aiming to detect failures and provide recommendations for optimum maintenance procedures. For larger systems, the data collected by the inverters provides the best source of information, and the cross-correlated analysis which uses these data is the best strategy to detect failures in module strings and failures in the inverters themselves (an average of 32.2% of inverters with failures was found after ten years of operation). In regards to determining which module is failing, the analysis of thermographic images is reliable and allows the detection of the failed module within the string (up to 1.5% for grave failures and 9.1% of medium failures for the solar plant after eleven years of activity). Photovoltaic (PV) systems at different scales require different methods for monitorization: Medium and large systems depend on inverter automated data acquisition, which can be complemented with thermographic images. Nevertheless, if the purpose of the monitorization is to obtain detailed information about the degradation processes of the solar cells, it becomes necessary to measure the environmental (irradiance and ambient temperature), thermal and electrical parameters (I-V characterization) of the modules and compare the experimental data with the modelling results. This is only achievable in small systems.
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Lu, Shi Qing, Han Bin Xiao, and Ping Deng. "Structural Fatigue Analysis of a Container Crane." Applied Mechanics and Materials 159 (March 2012): 272–76. http://dx.doi.org/10.4028/www.scientific.net/amm.159.272.
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This paper presents an approach to evaluate the structural fatigue life of port cranes. Based on a testing of 40t/45m container crane in a port of China, the stress time-history collection, data processing, the determination of S-N curve and the distribution of the load amplitude is analyzed in detail. Finally, the modified linear cumulative damage law is employed to evaluate the remaining safety service life of the container crane. The result of this study can be a reference for the anti-fatigue structural design of cranes, and it also provides a guideline for ports’ management and maintenance departments to make maintenance plans for port cranes.
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Blair, Jennifer, Bruce Stephen, Blair Brown, Alistair Forbes, and Stephen Mcarthur. "Hybrid Fault Prognostics for Nuclear Applications: Addressing Rotating Plant Model Uncertainty." PHM Society European Conference 7, no. 1 (June 29, 2022): 58–67. http://dx.doi.org/10.36001/phme.2022.v7i1.3321.
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Nuclear plant operators are required to understand the uncertainties associated with the deployment of prognostics toolsin order to justify their inclusion in operational decision making processes and satisfy regulatory requirements. Operationaluncertainty can cause underlying prognostics models to underperform on assets that are subject to evolving impactsof age, manufacturing tolerances, operating conditions, and operating environment effects, of which may be capturedthrough a condition monitoring (CM) system that itself may be degraded. Sources of uncertainty in the data acquisitionpipeline can impact the health of CM data used to estimate the remaining useful life (RUL) of assets. These uncertaintiescan disguise or misrepresent developing faults, where (for example) the fault identification is not achieved until it hasprogressed to an unmanageable state. This leaves little flexibility for the operator’s maintenance decisions and generallyundermines model confidence.
One method to quantify and account for operational uncertainty is calibrated hybrid models, employing physics, knowledgeor data driven methods to improve model accuracy and robustness. Hybrid models allow known physical relations tooffset full reliance on potentially untrustworthy data, whilst reducing the need for an abundance of representative historicaldata to reliably identify the monitored asset’s underlying behavioural trends. Calibration of the model then ensuresthe model is updated and representative of the real monitored asset by accounting for differences between the physics orknowledge model and CM data.
In this paper, an open-source bearing knowledge informed machine learning (ML) model and CM datasets are utilizedin an illustrative bearing prognostic application. The uncertainty incurred by the decisions made at key stages in thedevelopment of the model’s data acquisition and processing pipeline are assessed and demonstrated by the resultant impacton RUL prediction performance. It was shown that design decisions could result in multiple valid pipeline designswhich generated different predicted RUL trajectories, increasing the uncertainty in the model output.
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Cai, Wenfang, Songyuan Lu, Zhengfeng Wu, Guangyao Ying, and Wenjian Wu. "Strategy and Technique of High Efficiency Balancing in Field for Turbo-Generator Units with Large Capacity." Journal of Physics: Conference Series 2101, no. 1 (November 1, 2021): 012011. http://dx.doi.org/10.1088/1742-6596/2101/1/012011.
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Abstract Abstract.This paper aims at the high efficiency of field balancing for turbo-generator with large capacity currently, and introduces the strategies and key techniques of the rotor system balancing with practical cases of power plant in field. The acquisition, analysis and former processing of the original vibration data for balance calculation are included. Furthermore, they involve complete measuring points and conditions, accurate judgment for the types of unbalance exciting force and selection of stable vibration data, all these could reduce the blindness of balancing effectively. The strategies and techniques also contain the determination for axial plane of unbalance by the modal method, then the optimal steps and the plane of adding weight are chosen during the implementation of balancing. Besides, this paper also introduces the analysis and selection of influence coefficients and the phase of trial weight, these can help determine the final correction weight accurately in order to guarantee the balancing process prompt and efficient. Meanwhile, the restriction of practical location for adding weight and construction period of maintenance and production for the units should be considered during the high efficiency balancing in field. These strategies and techniques of high efficiency balancing have practical application value in promoting the technology of field balancing for turbo- generator units with large capacity.
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Hullo, J. F., G. Thibault, and C. Boucheny. "ADVANCES IN MULTI-SENSOR SCANNING AND VISUALIZATION OF COMPLEX PLANTS: THE UTMOST CASE OF A REACTOR BUILDING." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XL-5/W4 (February 18, 2015): 163–69. http://dx.doi.org/10.5194/isprsarchives-xl-5-w4-163-2015.
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In a context of increased maintenance operations and workers generational renewal, a nuclear owner and operator like Electricité de France (EDF) is interested in the scaling up of tools and methods of “as-built virtual reality” for larger buildings and wider audiences. However, acquisition and sharing of as-built data on a large scale (large and complex multi-floored buildings) challenge current scientific and technical capacities. <br><br> In this paper, we first present a state of the art of scanning tools and methods for industrial plants with very complex architecture. Then, we introduce the inner characteristics of the multi-sensor scanning and visualization of the interior of the most complex building of a power plant: a nuclear reactor building. We introduce several developments that made possible a first complete survey of such a large building, from acquisition, processing and fusion of multiple data sources (3D laser scans, total-station survey, RGB panoramic, 2D floor plans, 3D CAD as-built models). In addition, we present the concepts of a smart application developed for the painless exploration of the whole dataset. The goal of this application is to help professionals, unfamiliar with the manipulation of such datasets, to take into account spatial constraints induced by the building complexity while preparing maintenance operations. Finally, we discuss the main feedbacks of this large experiment, the remaining issues for the generalization of such large scale surveys and the future technical and scientific challenges in the field of industrial “virtual reality”.
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Le, Thi, Nóra Schreithofer, and Olli Dahl. "Dissolution Test Protocol for Estimating Water Quality Changes in Minerals Processing Plants Operating With Closed Water Circulation." Minerals 10, no. 8 (July 23, 2020): 653. http://dx.doi.org/10.3390/min10080653.
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To save freshwater resources and comply with environmental regulations, minerals processing operations are transitioning to partially or fully closed water circulation. However, the accumulation of electrolytes and the addition of reagents lead to changes in water composition and may compromise flotation performance and plant maintenance. As a consequence, costly modifications are often required to cope with these challenges. Therefore, knowledge about water quality variation owing to closed water circulation and its potential effect on the flotation performance is crucial. The experimental methodology presented in this paper targeted three main objectives: (1) predicting the tendency of the accumulation of elements and compounds into the process water during comminution, flotation, and storage in tailings facilities; (2) establishing a relationship between laboratory results and plant historical water quality data; and (3) predicting the potential effect of recycling water on flotation performance. The results obtained with Boliden Kevitsa ore showed a good correlation between the water matrix of the actual process water on-site and that obtained in the ore dissolution tests done in the laboratory. The final water composition came close to the process water in terms of major elements and some of the minor elements. Additionally, the work presented in this paper demonstrated that a dissolution loop allowed us to predict the potential impact of the recycling water on the ore flotability. This methodology could serve as an aid for predicting water quality matrix variation and designing closed water circulation systems at existing and new plants.
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Hullo, J. F., and G. Thibault. "Scaling up close-range surveys, a challenge for the generalization of as-built data in industrial applications." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XL-5 (June 6, 2014): 293–99. http://dx.doi.org/10.5194/isprsarchives-xl-5-293-2014.
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As-built CAD data reconstructed from Terrestrial Laser Scanner (TLS) data are used for more than two decades by Electricité de France (EDF) to prepare maintenance operations in its facilities. But today, the big picture is renewed: "as-built virtual reality" must address a huge scale-up to provide data to an increasing number of applications. In this paper, we first present a wide multi-sensor multi-purpose scanning campaign performed in a 10 floor building of a power plant in 2013: 1083 TLS stations (about 40.10<sup>9</sup> 3D points referenced under a 2 cm tolerance) and 1025 RGB panoramic images (340.10<sup>6</sup> pixels per point of view). As expected, this very large survey of high precision measurements in a complex environment stressed sensors and tools that were developed for more favourable conditions and smaller data sets. The whole survey process (tools and methods used from acquisition and processing to CAD reconstruction) underwent a detailed follow-up in order to state on the locks to a possible generalization to other buildings. Based on these recent feedbacks, we have highlighted some of these current bottlenecks in this paper: sensors denoising, automation in processes, data validation tools improvements, standardization of formats and (meta-) data structures.
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Nair, Abhilash, Aleksander Hykkerud, and Harsha Ratnaweera. "Estimating Phosphorus and COD Concentrations Using a Hybrid Soft Sensor: A Case Study in a Norwegian Municipal Wastewater Treatment Plant." Water 14, no. 3 (January 24, 2022): 332. http://dx.doi.org/10.3390/w14030332.
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Online monitoring of wastewater quality parameters is vital for an efficient and stable operation of wastewater treatment plants (WWTP). Several WWTPs rely on daily/weekly analysis of water samples rather than online automated wet-analyzers due to their high capital and maintenance costs. Soft-sensors are emerging as a viable alternative for real-time monitoring of parameters that either lack a reliable measuring principle or are measured using expensive online sensors. This paper presents the development, implementation, and validation of a hybrid soft sensor used to estimate Total Phosphorus (TP) and Chemical Oxygen Demand (COD) in the influent and effluent streams of a full-scale WWTP. A systematic method for cleaning and processing sensor data, identifying statistically significant correlations, and developing a mathematical model, is discussed. A non-intrusive Industrial Internet of Things (IIoT) infrastructure for soft-sensor deployment and a web-based GUI for data visualization are also presented in this work. The values of TP and COD estimated by the soft sensor are validated by comparing the estimated values to the daily average of their corresponding lab measurements. The data validation results demonstrate the potential of soft sensors in providing real-time values of essential wastewater quality parameters with an acceptable degree of accuracy.
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Semenov, A. P., and D. V. Kazarin. "Digitalization of traction rolling stock repairs." Herald of the Ural State University of Railway Transport, no. 1 (2020): 93–103. http://dx.doi.org/10.20291/2079-0392-2020-1-93-103.
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The article considers the approach to the mastering of modern digital technologies in the locomotive complex with regard to the tasks of increasing the controllability and, as a result, the efficiency of technological processes of maintenance and repair of traction rolling stock. The organizational, technical and technological solutions implemented in the first digital depot, created on the basis of the locomotive depot Vikhorevka of the East Siberian Traction Directorate of JSC «Russian Railways» (service locomotive depot Bratskoe of OOO «LokoTeh-Servis») are presented. Joint application of the complex of implemented solutions ensures implementation of the new technology of organization of locomotives maintenance and repair, feasibility of reaching the targets for readiness of the traction rolling stock for operation. Achieving efficiency of the technology built within the framework of the Digital Depot project is ensured by the universality of the repair position - all technological operations on locomotives maintenance and repair are performed at the digital universal repair position, including supercycle unscheduled works, are carried out in a single place without additional maneuvering. The universality of repair positions together with application of radio-controlled self-propelled traction unit with hybrid power plant increases the depot energy efficiency, reduces the expenses for maintenance of shunting locomotives stock and their total number, as well as allows to increase rhythmicity of locomotive repair. Implementation of transparent comprehensive information exchange between the process equipment of repair position, the systems for collecting, processing, analyzing data and information obtained in real time from on-board locomotive systems, monitoring and diagnostics systems applied during locomotive acceptance for repair, input and operation control, as well as at the stage of output control, provides the efficiency of control actions, objectivity of personnel performance discipline control and compliance with technical processes, increases the practical value of diagnostic data, which form the basis of qualitative repair and reliability of locomotives in operation.
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Roumpakias, Elias, and Tassos Stamatelos. "Health Monitoring and Fault Detection in Photovoltaic Systems in Central Greece Using Artificial Neural Networks." Applied Sciences 12, no. 23 (November 24, 2022): 12016. http://dx.doi.org/10.3390/app122312016.
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The operation and maintenance of a photovoltaic system is a challenging task that requires scientific soundness, and has significant economic impact. Faults in photovoltaic systems are a common phenomenon that demands fast diagnosis and repair. The effective and accurate diagnosis and categorization of faults is based on information received from the photovoltaic plant monitoring and energy management system. This paper presents the application of machine learning techniques in the processing of monitoring datasets of grid connected systems in order to diagnose faults. In particular, monitoring data from four photovoltaic parks located in Central Greece are analyzed. The existing data are divided for training and validation procedures. Different scenarios are examined first, in order to observe and quantify the behavior of artificial neural networks in already known faults. In this process, the faults are divided in three main categories. The system’s performance deviation against the prediction of the trained artificial neural network in each fault category is processed by health monitoring methodology in order to specify it quantitatively.
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Sarno, Sarno. "PEMBERDAYAAN KARANG TARUNA DESA RAKIT MELALUI KEGIATAN BUDIDAYA DAN PENGOLAHAN SAYURAN ORGANIK." Masyarakat Madani: Jurnal Kajian Islam dan Pengembangan Masyarakat 4, no. 2 (December 10, 2019): 33. http://dx.doi.org/10.24014/jmm.v4i2.7898.
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AbstrakTujuan penelitian adalah mengkaji kegiatan pemberdayaan pada karang taruna Desa Rakit tentang budidaya dan pengolahan sayuran organik yang mengarah pada peningkatan usaha ekonomi produktif. Metode yang digunakan adalah deskriptif. Metode pengumpulan data yang dilakukan melalui kegiatan wawancara, pencatatan,kegiatan observasi yaitu pengumpulan data dengan cara melakukan pengamatan secara langsung terhadap obyek kegiatan. Jenis data yang dikumpulkan adalah data primer dan data sekunder. Hasil penelitian sebagai berikut; (a) para pengurus dan anggota fokus pada kegiatan pengembangan potensi dan penggalian kemampuan secara terarah pada upaya peningkatan keterampilan dibidang kewirausahaan, peningkatan pengetahuan teknik budidaya dan pengolahan sayuran organik secara baik dan benar; (b) Pemberdayaan karang taruna dilakukan melalui kegiatan pendidikan yang diimplementasikan dalam kegiatan penyuluhan, pemberian materi dan penjelasan materi tentang pentingnya organik, teknik budidaya dan pengolahan sayuran organik, kemudian melalui kegiatan pelatihan tentang pembuatan media tanam, teknik penanaman, pemeliharaan tanaman, panen dan pengolahan sayuran organik serta kegiatan pendampingan berkelanjutan tentang kewirausahaan dan peningkatan usaha ekonomi produktif.Kata kunci: Karang Taruna, Budidaya, Sayuran, Organik AbstractThe purpose of this study is to examine the empowerment activities of the Rakit Village youth organizations on the cultivation and processing of organic vegetables that lead to increased productive economic endeavors. The method used is descriptive. Data collection methods are carried out through interviews, recording, observation activities, namely data collection by directly observing the object of activity. The type of data collected is primary data and secondary data. The results of the study are as follows; (a) management and members focus on developing potentials and exploring capabilities in an effort to improve skills in the field of entrepreneurship, increasing knowledge of cultivation techniques and processing of organic vegetables properly and correctly; (b) Youth empowerment is carried out through educational activities implemented in extension activities, providing material and explaining material about the importance of organic, organic vegetable cultivation and processing techniques, then through training activities on making planting media, planting techniques, plant maintenance, harvesting and processing organic vegetables and sustainable assistance activities on entrepreneurship and the promotion of productive economic ventures.Keywords: Youth Organization, Aquaculture, Vegetables, Organic
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Dotlačil, L., Z. Stehno, I. Fáberová, and A. Michalová. "Research, conservation and utilisation of plant genetic resources and agro-biodiversity enhancement – Contribution of the Research Institute of Crop Production Prague-Ruzyně." Czech Journal of Genetics and Plant Breeding 38, No. 1 (July 30, 2012): 3–15. http://dx.doi.org/10.17221/6106-cjgpb.
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Activities on plant genetic resources in the Czech Republic are concentrated in the National Programme on Conservation and Utilisation of Plant Genetic Resources. Eleven institutions maintain 51,000 accessions, of which 17.3% belong to vegetatively propagated species. Research Institute of Crop Production (RICP) Prague has the responsibility for the coordination of the Programme; it holds more than half of all accessions in collections of genetic resources, runs the national information system and provides long-term storage for all seed-propagated species. All Czech collections are fully documented in passport data. Evaluation data (based on National descriptor lists for 29 crops) are available for 33% of the accessions. Much work is currently spent to the description and evaluation of collections, to facilitate their utilisation in breeding and agricultural practice. Also collecting missions on the Czech territory, conservation and monitoring of valuable resources maintained in situ contributeto the maintenance and evaluation of local resources. Landraces are considered a valuable part of the collections. Suitable ways of “on farm” conservation are investigated for selected accessions, to support their utilisation in agricultural practice and to enrich the existing diversity of crops and cultivars. Cultivars and landraces of neglected crops (buckwheat, millet, hulled wheat species) were successfully used to enrich the agro-biodiversity and for specific purposes of human nutrition. Close collaboration with producers (often organic farms) and processing industry has been established. Selected alternative crops and catch crops were studied as potential new crops.
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Figueiredo, Vila, Fiúza, Góis, Futuro, Dinis, and Martins. "A Holistic Approach in Re-Mining Old Tailings Deposits for the Supply of Critical-Metals: A Portuguese Case Study." Minerals 9, no. 10 (October 17, 2019): 638. http://dx.doi.org/10.3390/min9100638.
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Demand growth for metallic minerals has been faced with the need for new techniques and improving technologies for all mining life-cycle operations. Nowadays, the exploitation of old tailings and mine-waste facilities could be a solution to this demand, with economic and environmental advantages. The Panasqueira Mine has been operating for more than a century, extracting tungsten and tin ore. Its first processing plant, “Rio”, was located near the Zêrere river, where mineral-processing residues were deposited on the top hillside on the margin of this river in the Cabeço do Pião tailings dam. The lack of maintenance and monitoring of this enormous structure in the last twenty years represents a high risk to the environment and the population of the surrounding region. A field-sample campaign allowed the collection of data, and resulted from laboratory tests to use regression optimization. Re-mining the tailings by hydrometallurgical methods was considered to satisfy the two conditions of metal demand and environmental risk. The metal content in Cabeço do Pião was shown be enough for environmental restoration. The re-mining solution was studied, taking into account the technical, economic, social, and environmental aspects.
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Geng, X., S. Xing, and Q. Xu. "A GENERIC RIGOROUS SENSOR MODEL FOR PHOTOGRAMMETRIC PROCESSING OF PUSHBROOM PLANETARY IMAGES." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLII-2/W13 (June 5, 2019): 1389–96. http://dx.doi.org/10.5194/isprs-archives-xlii-2-w13-1389-2019.
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<p><strong>Abstract.</strong> Currently, each planetary exploration mission team always develops its own software modules to support the photogrammetric processing of planetary images, and as a result of that the main drawbacks are lacking software reusability and the high cost of software development and maintenance. This is mainly due to that there is lack of a highly universal sensor model in the planetary mapping community. This paper presents a generic rigorous sensor model (RSM) for the photogrammetric processing of pushbroom planetary images. The main contributions of this paper include: (1) the implementation details of the generic RSM; (2) the optimized coordinates transformation methods between 3D ground points and 2D image points for linear pushbroom images; (3) a pipeline to acquire exterior orientation (EO) parameters for each planetary image. The generic RSM is developed based on the methodology used in airborne linear scanners ADS40. Specifically, the generic RSM comprises of a camera file and an orientation data file for each image. The camera file stores each detector’s calibrated image coordinates and the orientation data file contains each scan line’s EO parameters, such that the RSM can perform coordinates transformation among pixel coordinates, focal plane coordinates and ground coordinates. Furthermore, the generic RSM supports varying exposure time, summing mode and image distortions, which are typical problems that need to be solved in planetary mapping. We tested the generic RSM with Lunar Reconnaissance Orbiter (LRO) Narrow Angle Camera (NAC), Chandrayaan-1 Moon Mineralogy Mapper (M3) and Mars Express (MEX) High Resolution Stereo Camera (HRSC) images. The geometric accuracy and computational efficiency of the developed generic RSM were compared with the famous planetary mapping software, namely Integrated System for Imagers and Spectrometers (ISIS). The experimental results demonstrate that the generic RSM has the merits of processing various types of pushbroom planetary images with a unified way and decreasing the software development and maintenance burden. Moreover, the developed generic RSM significantly improves the computational efficiency of orthophoto generation and tie points extraction for pushbroom planetary images.</p>