Relevant bibliographies by topics / Plant maintenance Data processing

Academic literature on the topic 'Plant maintenance Data processing'

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Published: 10 December 2022
Last updated: 29 January 2023

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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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Dissertations / Theses on the topic "Plant maintenance Data processing"

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Liu, Bin. "Optimization strategies for data warehouse maintenance in distributed environments." Link to electronic thesis, 2002. http://www.wpi.edu/Pubs/ETD/Available/etd-0430102-133814.

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Numanovic, Kerim. "Advanced Clinical Data Processing: A Predictive Maintenance Model for Anesthesia Machines." Thesis, KTH, Tillämpad fysik, 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-283323.

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The maintenance of medical devices is of great importance to ensure that the devices are stable, well-functioning, and safe to use. The current method of maintenance, which is called preventive maintenance, has its advantages but can be problematic both from an operators’ and a manufacturers’ side. Developing a model that will predict failure in anesthesia machines can be of great use for the manufacturer, the customers, and the patients. This thesis sets to examine the possibility of creating a predictive maintenance model for anesthesia machines by utilizing device data and machine learning. This thesis also investigates the influence of the data on the model performance and compare different lag sizes and future horizons to model performance. The time-series data collected came from 87 unique devices and a specific test was chosen to be the output variable of the model. A whole pipeline was created, which included pre-processing of the data, feature engineering, and model development. Feature extraction was done on the time series data, with the help of a library called tsfresh, which transformed time series characteristics into features that would enable supervised learning. Two models were developed: logistic regression and XGBoost. The logistic regression model acted as a baseline model and the result of its performance was as expected, quite poor. The XGBoost yielded an AUCPR score of 0.21 on the full dataset and 0.32 on a downsampled dataset. Although a quite low score, it was surprisingly high considering the extreme class imbalance that existed in the dataset. No clear pattern was found between the lag sizes and future horizons with the model performance. Something that could be seen was that the data imbalance had a great impact on the model performance, which was discovered when the downsampled dataset with less class imbalance yielded a higher AUCPR score.
Underhållet av medicintekniska produkter är mycket viktigt för att säkerställa att enheterna är stabila, välfungerande och säkra att använda. Den nuvarande underhållsmetoden, som kallas förebyggande underhåll, har sina fördelar men kan vara problematisk både från operatörens och tillverkarsidan. Att utveckla en modell som förutsäger fel i anestesimaskiner kan vara till stor nytta för tillverkaren, kunderna och patienterna. Denna avhandling syftar till att undersöka möjligheten att skapa en förutsägbar underhållsmodell för anestesimaskiner genom att använda enhetsdata och maskininlärning. Denna avhandling undersöker också påverkan av data på modellprestanda och jämför olika fördröjningsstorlekar och framtida horisonter med modellprestanda. Tidsseriedata som samlats in kom från 87 unika enheter och ett specifikt test valdes för att vara modellens outputvariabel. En hel pipeline skapades, som inkluderade förbehandling av data, funktionsteknik och modellutveckling. Funktionsextraktion gjordes på tidsseriedata med hjälp av ett bibliotek som heter tsfresh, som förvandlade tidsserieegenskaper till funktioner som skulle möjliggöra övervakat lärande. Två modeller utvecklades: logistisk regression och XGBoost. Den logistiska regressionsmodellen fungerade som en basmodell och resultatet av dess prestanda var som förväntat ganska dåligt. XGBoost gav en AUCPR-poäng på 0,21 på hela datamängden och 0,32 på en nedmonterad datamängd. Även om det var en ganska låg poäng, var det överraskande högt med tanke på den extrema klassobalansen som fanns i datasetet. Inget tydligt mönster hittades mellan fördröjningsstorlekarna och framtida horisonter med modellprestanda. Något som kunde ses var att dataobalansen hade stor inverkan på modellens prestanda, vilket upptäcktes när den nedprovade datamängden med mindre obalans i klassen gav en högre AUCPR-poäng.
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Antonovsky, Ari David. "The relationship between human factors and plant maintenance reliability in a petroleum processing organisation." Thesis, Curtin University, 2010. http://hdl.handle.net/20.500.11937/336.

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Despite the considerable emphasis on improving maintenance reliability in the petroleum industry by adopting an engineering approach (International Standards Organization, 2006b), production losses, ineffective maintenance, and major disasters continue to occur (Urbina, 2010; Pidgeon, 2000). Analyses of these events have indicated that a failure to consider the human factors in the design (Taylor, 2007), operation (Øien, 2001a), or maintenance (Bea, 1998) of hazardous process technologies is often an important contributor. Based on research to evaluate the influence of these human factors on organisational performance, various models (Rasmussen, 1982; Dekker 2005) and taxonomies (Reason, 1998) for analysing organisational processes at the individual-, group- and organisational-level have been developed.By using these models, the current research was designed to determine the influence of human factors on maintenance reliability in petroleum operations. Three studies were conducted in petroleum operations with the objective in the first two studies of identifying the most-frequent contributors to maintenance-related failures, and in the third study, determining if group differences between higher and lower reliability work areas could be differentiated on the basis of these human factors.In Study 1, the First Priority incident database of the target organisation was used to determine the most frequently reported human factors in maintenance-related, lost-production failures. The most-frequent factors in the incidents (N=194) were found to be Violations, Design & Maintenance, Detection, and Decision-making. These results accorded with earlier studies in the field of human factors (Hobbs & Williamson, 2003; Lawton 1998), which frequently identified human error and violations as the causes of failures. Study 2 provided a more rigorous investigation of the organisational contributors to failures through structured interviews with maintenance personnel. The results of these interviews (N=38) using the Human Factors Investigation Tool (HFIT) (Gordon, 2005) demonstrated that Assumption, Design & Maintenance, and Communication were the most frequent contributors to maintenance-related failures.Based on the predominant factors identified in Study 2, a survey of the perceptions of maintenance personnel (N=178) was conducted for Study 3. Scales measuring Problem-solving (Morgeson & Humphrey, 2006) and Vigilance (Mann, Burnett, Radford, & Ford, 1997) were used to measure the processes that provoke assumptions. Design & Maintenance items from HFIT (Gordon, 2001), and scales from Wiio’s (1978 a&b) Organisational Communication Development questionnaire (OCD/2) were used to test the factors identified in Study 2. Exploratory Factor Analysis indicated that the responses to the Design & Maintenance items loaded onto a single variable, while the Communication items loaded onto two variables, which were named Job-related feedback and Information about change.The perceptions of personnel in lower and higher reliability work areas across the target organisation were compared using these scales, with reliability level ranked according to the monthly Mean Time Between Deferments of petroleum production. Significant between-group differences were found between work areas on Design & Maintenance and Problem-solving. These results suggest that better maintainability in the design of plant is predictive of higher reliability level. In addition, greater requirements for Problem-solving were associated with lower reliability level. There were no significant effects of reliability on Vigilance or either communication measure.The quantitative data was triangulated with comments in response to an open-ended question asking about factors that help or hinder maintenance activities. Respondent’s comments indicated that Communication was not significantly associated with reliability at the group-level. The reason appeared to be that Communication was an organisation-level property of the employing company. Many comments indicated that access to information was difficult, explaining the high occurrence of assumptions reported in Study 2. In addition, although maintenance personnel generally agreed in the survey that they were vigilant in decision-making, personnel in lower reliability facilities provided a higher proportion of comments indicating that the decision-making of supervisors and management had a negative impact on their work.The results of the three studies support past research demonstrating that problem-solving skills (Tucker, 2002) and the design of socio-technical facilities (Reiman, Oedewald & Rollenhagen, 2005) have an important influence on organisational performance. The findings further extend research in the field of human factors by demonstrating a significant relationship between these two factors and group-level performance. The findings also demonstrated the importance of organisational communication, but as an organisational-level dimension that might not influence group-level measures. This research has implications for organisations that operate complex, hazardous technologies and that are attempting to improve organisational processes by utilising a human factors approach.
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Johansson, Peter. "Plant Condition Measurement from Spectral Reflectance Data." Thesis, Linköping University, Computer Vision, 2010. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-59286.

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The thesis presents an investigation of the potential of measuring plant condition from hyperspectral reflectance data. To do this, some linear methods for embedding the high dimensional hyperspectral data and to perform regression to a plant condition space have been compared. A preprocessing step that aims at normalized illumination intensity in the hyperspectral images has been conducted and some different methods for this purpose have also been compared.A large scale experiment has been conducted where tobacco plants have been grown and treated differently with respect to watering and nutrition. The treatment of the plants has served as ground truth for the plant condition. Four sets of plants have been grown one week apart and the plants have been measured at different ages up to the age of about five weeks. The thesis concludes that there is a relationship between plant treatment and their leaves' spectral reflectance, but the treatment has to be somewhat extreme for enabling a useful treatment approximation from the spectrum. CCA has been the proposed method for calculation of the hyperspectral basis that is used to embed the hyperspectral data to the plant condition (treatment) space. A preprocessing method that uses a weighted normalization of the spectrums for illumination intensity normalization is concluded to be the most powerful of the compared methods.

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Kaidis, Christos. "Wind Turbine Reliability Prediction : A Scada Data Processing & Reliability Estimation Tool." Thesis, Uppsala universitet, Institutionen för geovetenskaper, 2014. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-221135.

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This research project discusses the life-cycle analysis of wind turbines through the processing of operational data from two modern European wind farms. A methodology for SCADA data processing has been developed combining previous research findings and in-house experience followed by statistical analysis of the results. The analysis was performed by dividing the wind turbine into assemblies and the failures events in severity categories. Depending on the failure severity category a different statistical methodology was applied, examining the reliability growth and the applicability of the “bathtub curve” concept for wind turbine reliability analysis. Finally, a methodology for adapting the results of the statistical analysis to site-specific environmental conditions is proposed.
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李淑儀 and Shuk-yee Wendy Lee. "Computer aided facilities design." Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 1987. http://hub.hku.hk/bib/B31208277.

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Samson, Margaret Kingman 1950. "COMPUTER AIDS FOR FACILITY LAYOUT." Thesis, The University of Arizona, 1987. http://hdl.handle.net/10150/276400.

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Mendez, Ronald Osiris. "The building information model in facilities management." Link to electronic thesis, 2006. http://www.wpi.edu/Pubs/ETD/Available/etd-050406-153423/.

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Martello, Rosanna. "Cloud storage and processing of automotive Lithium-ion batteries data for RUL prediction." Master's thesis, Alma Mater Studiorum - Università di Bologna, 2021.

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Lithium-ion batteries are the ideal choice for electric and hybrid vehicles, but the high cost and the relatively short life represent an open issue for automotive industries. For this reason, the estimation of battery Remaining Useful Life (RUL) and the State of Health (SoH) are primary goals in the automotive sector. Cloud computing provides all the resources necessary to store, process and analyze all sensor data coming from connected vehicles in order to develop Predictive Maintenance tasks. This project describes the work done during my internship at FEV Italia s.r.l. The aims were designing an architecture for managing the data coming from a vehicle fleet and developing algorithms able to predict the SoH and the RUL of Lithium-ion batteries. The designed architecture is based on three Amazon Web Services: Amazon Elastic Compute Cloud, Amazon Simple Storage Service and Amazon Relational Database Service. After data processing and the feature extraction, the RUL and SoH estimations are performed by training two Neural Networks.
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Evans, Roy F. "Industrial maintenance data collection and application developing an information strategy for an industrial site /." Access electronically, 2008. http://ro.uow.edu.au/theses/92.

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Books on the topic "Plant maintenance Data processing"

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Plant maintenance with SAP. 2nd ed. Bonn [Germany]: Galileo Press, 2010.

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Liebstuckel, Karl. Configuring SAP plant maintenance. Bonn: Galileo Press, 2014.

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Liebstuckel, Karl. Plant maintenance with SAP: Practical guide. Bonn: Galileo, 2014.

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CMMS: A timesaving implementation process. Boca Raton, Fla: CRC Press, 2003.

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Maintenance computerization handbook. Lilburn, GA: Fairmont Press, 1993.

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Microcomputer-aided maintenance management. New York: M. Dekker, 1987.

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Equipment management in the post-maintenance era: A new alternative to total productive maintenance (TPM). Boca Raton, FL: Taylor & Francis, 2012.

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Holmberg, K. E-maintenance. London: Springer, 2010.

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Weingärtner, J. EDV-gestützte Instandhaltung: Gestaltung und Auslegung von rechnergestützten Systemen. Berlin: Springer, 1988.

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Breer, U. Auswahl und Beurteilung EDV-gestützter IPS-Systeme. Berlin: Springer, 1989.

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Book chapters on the topic "Plant maintenance Data processing"

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Södergård, Caj. "Summary of Potential and Exploitation of Big Data and AI in Bioeconomy." In Big Data in Bioeconomy, 417–23. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-71069-9_32.

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AbstractIn this final chapter, we summarize the DataBio learnings about how to exploit big data and AI in bioeconomy. The development platform for the software used in the 27 pilots was a central tool. The Enterprise Architecture model Archimate laid a solid basis for the complex software in the pilots. Handling data from sensors and earth observation were shown in numerous pilots. Genomic data from crop species allows us to significantly speed up plant breeding by predicting plant properties in-silico. Data integration is crucial and we show how linked data enables searches over multiple datasets. Real-time processing of events provides insights for fast decision-making, for example about ship engine conditions. We show how sensitive bioeconomy data can be analysed in a privacy-preserving way. The agriculture pilots show with clear numbers the impact of big data and AI on precision agriculture, insurance and subsidies control. In forestry, DataBio developed several big data tools for forest monitoring. In fishery, we demonstrate how to reduce maintenance cost and time as well as fuel consumption in the operation of fishing vessels as well as how to accurately predict fish catches. The chapter ends with perspectives on earth observation, machine learning, data sharing and crowdsourcing.
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Chen, Tian-lu, and Rui Dai. "Metabolomic Data Processing Based on Mass Spectrometry Platforms." In Plant Metabolomics, 123–69. Dordrecht: Springer Netherlands, 2014. http://dx.doi.org/10.1007/978-94-017-9291-2_6.

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Wei, Chih-Ping, Hung-Chen Chen, Ching-Tun Chang, and Yen-Ming Chu. "Supporting Patent Maintenance Decision: A Data Mining Approach." In Lecture Notes in Business Information Processing, 87–99. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-29873-8_9.

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Rousopoulou, Vaia, Alexandros Nizamis, Luigi Giugliano, Peter Haigh, Luis Martins, Dimosthenis Ioannidis, and Dimitrios Tzovaras. "Data Analytics Towards Predictive Maintenance for Industrial Ovens." In Lecture Notes in Business Information Processing, 83–94. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-20948-3_8.

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Zhou, Jing, Yijie Wang, and Sikun Li. "Key-Attributes Based Optimistic Data Consistency Maintenance Method." In Parallel and Distributed Processing and Applications, 91–103. Berlin, Heidelberg: Springer Berlin Heidelberg, 2007. http://dx.doi.org/10.1007/978-3-540-74742-0_11.

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Agrawa, Divyakant, and Amr El Abbadi. "View Maintenance and Analytical Processing at Data Warehouses." In Databases in Networked Information Systems, 107–18. Berlin, Heidelberg: Springer Berlin Heidelberg, 2000. http://dx.doi.org/10.1007/3-540-44431-9_8.

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Gilabert, E., E. Jantunen, C. Emmanouilidis, A. Starr, and A. Arnaiz. "Optimizing E-Maintenance Through Intelligent Data Processing Systems." In Lecture Notes in Mechanical Engineering, 1–9. London: Springer London, 2013. http://dx.doi.org/10.1007/978-1-4471-4993-4_1.

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Fiehn, Oliver, Tobias Kind, and Dinesh Kumar Barupal. "Data Processing, Metabolomic Databases and Pathway Analysis." In Annual Plant Reviews Volume 43, 367–406. Oxford, UK: Wiley-Blackwell, 2011. http://dx.doi.org/10.1002/9781444339956.ch12.

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Zhuang, Zhenyun, Min Shen, Haricharan Ramachandra, and Suja Viswesan. "Choosing Optimal Maintenance Time for Stateless Data-Processing Clusters." In Job Scheduling Strategies for Parallel Processing, 252–73. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-61756-5_14.

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Deksne, Līva, Jānis Grabis, and Edžus Žeiris. "Towards Data Ecosystem Based Winter Road Maintenance ERP System." In Lecture Notes in Business Information Processing, 69–83. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-87205-2_5.

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Conference papers on the topic "Plant maintenance Data processing"

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El-Gheriani, Hany, Martin Guay, Guillaume Graton, and Jorge Arinez. "Identifying Correlations Between Independent Sets of Maintenance and Manufacturing Quality Data." In ASME 2008 International Manufacturing Science and Engineering Conference collocated with the 3rd JSME/ASME International Conference on Materials and Processing. ASMEDC, 2008. http://dx.doi.org/10.1115/msec_icmp2008-72030.

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Effective equipment maintenance is essential for a manufacturing plant seeking to produce high quality products. The impact of equipment reliability and quality on throughput have been well established, however, the relationship between maintenance and quality is not always clear or direct. This paper describes a statistical modeling method that makes use of a Kalman filter to identify correlations between independent sets of maintenance and quality data. With such a method, maintenance efforts can be better prioritized to satisfy both production and quality requirements. In addition, this method is used to compare results from the theoretical maintenance-quality model to data from an actual manufacturing system. Results of the analysis indicate the potential for this method to be applied to preventive as well as reactive maintenance decisions since ageing aspects of equipment are also considered in the model.
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Borrison, David E. "Strategic Automation for Citrus Processing." In ASME 2000 Citrus Engineering Conference. American Society of Mechanical Engineers, 2000. http://dx.doi.org/10.1115/cec2000-4601.

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AUTOMATION: Automatically controlled operation of an apparatus, process, or system by mechanical or electronic devices that take the place of human organs of observation, effort, and decision. (Merriam-Webster) STRATEGIC: Of great or vital importance within an integrated whole or to the taking place of a planned or unplanned occurrence. (Merriam-Webster) Strategic automation requires a comprehensive plan for automating the entire plant. This master plan must be followed as individual areas are automated and incorporated into the overall plan. The master plan will set all the standards for process and instrument drawings, equipment identification, instrument tags, control systems, operator interface, and control room locations. With a master plan the following advantages are realized; * Newly automatedareas are easily integrated into the existing infrastructure. * Fewer operators controlling multiple process areas from a single control room. * Standardized operator interfaces minimize operator training and cross training. * Maintenance personnel require less training because of common equipment. * Spare parts inventories are reduced. * Process changes or upsets can be sent throughout the system to adjust other affected areas. * Monitoring and recording data from any area of the plant is easily accomplished. Without a master plan none of the advantages are guaranteed. The cost to link and maintain different systems can be more expensive than the original cost of the system. Exchanging data between systems, or capturing and recording data across multiple systems can become very complex and expensive. Even the best designed, best of breed individual systems don’t always tie together to form a complete and comprehensive overall plant system. Once the master plan is in place, all areas of a citrus plant can be automated. Some areas can be fully automated while others are better controlled by partial automation with enhanced information made available to the operators. Some decisions are driven by measurement instrument availability, while others are purely economical. The ultimate goals are better and more consistent product quality with fewer operating personnel, a process that responds automatically plant wide to changes, and data gathering to monitor and improve your entire process. This paper will address the typical process areas in a citrus plant and discuss viable control techniques as they apply to each area. It will also discuss the interactions between processing areas and the monitoring of the plant as a whole. Paper published with permission.
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Drinkwater, Ben J., Chris Brashears, and Dennis G. Wilson. "Development of a Portable Field Machinery Data Acquisition System." In ASME 1985 International Gas Turbine Conference and Exhibit. American Society of Mechanical Engineers, 1985. http://dx.doi.org/10.1115/85-gt-95.

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This paper describes the development of a portable data acquisition system for use in a plant-wide, predictive maintenance program for rotating machinery. Designed to replace hand-logging and other clipboard surveillance methods, the portable instrument stores the measured parameters in digital memory for transfer to a desktop host computer for processing and analysis. Developments in electronics and computer technology as well as preventive/predictive maintenance procedures have made this instrument system possible. These developments are outlined, along with a discussion of the operation and use of the system.
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Jansky, Andy, and Magnus Langenstein. "Nuclear Regulatory Acceptance of Certified Process Data Reconciliation (CPDR)." In 2020 International Conference on Nuclear Engineering collocated with the ASME 2020 Power Conference. American Society of Mechanical Engineers, 2020. http://dx.doi.org/10.1115/icone2020-16525.

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Abstract The utility industry is currently undergoing a substantial change from an analog to a digital infrastructure. Not only plant performance and utility profit are dependent on accurate plant operational parameters but, more importantly, set safety limits need to be met in order to ensure safe operation of nuclear power plants in particular. Using non-quality-assured process data for operational decisions can result in significant over- or under-power of the plant. In addition, all new technologies such as AI, IIoT, digital twin technology, etc. rely on robust process data as input, putting at risk the significance of the results from the continuing data processing (“garbage in, garbage out”). One method, certified process data reconciliation, or CPDR, cuts through the vast amount of available process data and generates all relevant process values with the smallest uncertainty possible. 95% of all collected process data can be discarded after introduction of CPDR. With CPDR, plant operation and maintenance can be significantly optimized and utilities can profit by realizing e.g. power recovery and measurement uncertainty recapture (MUR). Because the focus on reconciled instead of measured values constitutes a paradigm shift, the application of CPDR needs to be communicated to Nuclear Regulators. This paper describes the approach and experience of the Regulator acceptance process in various countries around the globe.
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Liu, Chun-yang, Ji-shun Li, Yu-jun Xue, Wei Ma, Xin Sui, Fang Yang, and Xi-qiang Ma. "Development of On-Line Condition Monitoring System for Wind Turbines in Plant Level." In ASME 2015 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. American Society of Mechanical Engineers, 2015. http://dx.doi.org/10.1115/detc2015-46244.

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The maintenance cost of wind turbines has become the significant challenge for the wind power enterprise. One available way to reduce the cost is to monitor the wind turbines and make preventative maintenance. In this paper, an on-line condition monitoring system for wind turbines in plant level was presented. A 4-level monitoring structure was designed for the system, and the network communication was used to transmit the data efficiently. A high performance digital signal processor (DSP) was used as the processing core in the signal acquisition device. A computer software was also developed for data analysis and remote management of the monitoring device. The vibrations, temperature, and some other information of the turbines were gathered synchronously. The monitoring center in wind farm, the data center of wind power enterprise and the remote service center of equipment supplier can know the operation condition of wind turbines well. It would be useful for wind power enterprise to obtain the operation condition of wind turbine and reduce the cost of maintenance.
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Cˇilli´k, Ivan, Ja´n Procha´ska, and Peter Halada. "VVER 440 Reliability Data Used for PSA Studies." In 10th International Conference on Nuclear Engineering. ASMEDC, 2002. http://dx.doi.org/10.1115/icone10-22703.

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The paper presents the methods used for the reliability parameter calculations. The method includes full procedure of a plant specific data gathering and processing for the purposes for full and low power conditions and shutdown states for V1 WWER 440/V230 and V2 WWER 440/V213 located in Jaslovske Bohunice, Slovakia. Presented calculations include unavailability due to maintenance and test as well as some special cases. More detail description of sources plant specific data used for final elaboration as well as software used for reliability parameters calculation is given. Different software output files present data formats required not only by PSA models including calculation background for defined component groups. Based on the quality of the presented VVER 440 data VU´JE Trnava was contracted by U.S. DOE to support its PSA activities in Russia.
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Horn, Agnes Marie, Frode Wiggen, and Sandra Gustafsson. "Sensor Data for Maintenance Planning of Topside Piping." In ASME 2022 41st International Conference on Ocean, Offshore and Arctic Engineering. American Society of Mechanical Engineers, 2022. http://dx.doi.org/10.1115/omae2022-81319.

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Abstract The wish to better know the condition of equipment and thus be able to plan and perform maintenance before a critical failure occurs has existed for many years. Early leak detection of liquid and gas from topside process piping is a critical task for economical and safety reasons. For an operator, there is a need to accurately detect, and localize leaks to timely recover them cost-effectively, or the most viable solution to be able to prevent leaks to happen. The latter named prognostic maintenance, condition-based maintenance or predictive maintenance represents a well-known method where the goal is to ensure preventive measures are taken in due time where leak may happen in a piping system. There is a rapid development in sensor technologies, algorithm development, data storage systems, data transfer, data processing and computing power. The robustness and reliability of electronic components is constantly improving while the cost is decreasing. To ‘unhide’ hidden failures through condition knowledge and thus reduce inspection and unnecessary repairs and testing efforts on critical (e.g. safety) equipment is a compelling need for the Energy sector today. Digitalization is currently boosting this technology both within model-based prognostics, data-driven prognostics, and hybrid solutions. To make use of this development, classical understanding of fatigue, corrosion and moisture penetration on piping systems must be combined with several IT-related disciplines. The paper will discuss the latest state-of-the art related to sensor monitoring systems with the aim to provide sound recommendations related to fatigue, corrosion and moisture penetration on piping system for topside process piping.
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Barnekov, Ulf, Matthias Bauroth, and Michael Paul. "Decontamination and Decommissioning of the Uranium Mill and Processing Plant at Seelingstaedt, Germany." In The 11th International Conference on Environmental Remediation and Radioactive Waste Management. ASMEDC, 2007. http://dx.doi.org/10.1115/icem2007-7380.

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In Eastern Germany uranium mining lasted from 1946 till 1990 including a production of in total 220,000 t of uranium. The Seelingsta¨dt Uranium Mill and Processing Plant, located in Thuringia, Germany, was one of two large uranium mills owned by Wismut. The mill was erected by 1960 and covered an area of 93 ha. From 1961 till 1991 a total of about 110 million t of different types of uranium ores were milled and processed at the Seelingsta¨dt mill. The mill produced ca. 110,000 t of uranium (in yellow cake). Demolition of the buildings and industrial facilities of the Seelingsta¨dt mill and processing plant site are nearly completed. The site is being decommissioned with respect to after-use aiming at afforestation and grasslands allowing for a stable plant succession. Decommissioning includes excavation and relocation of contaminated materials, reshaping of the site and construction of ditches for granting a stable surface runoff as well construction of access and maintenance roads. About 85% of the demolition and relocation works have been completed till to date. Last decommissioning works shall be completed by 2015. The present paper presents experiences made and progress achieved till to date.
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Pierson, Nicholas, Richard Clarke, and Oleg Mikhailov. "Transforming Asset Maintenance and Optimisation Through Autonomous Operations." In Offshore Technology Conference. OTC, 2022. http://dx.doi.org/10.4043/31847-ms.

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Abstract Autonomous Operations is about utilising the latest available fit-for-purpose technology in operations, maintenance, inspection, and asset optimisation. It involves collecting operating data such as visual, thermal, low- and high-frequency acoustic, FLIR (Forward Looking InfraRed), and gas-sensing through devices mounted on autonomous robots or drones, possibly mixed with stand-alone fixed devices. This "mobile sensor" approach is ideal for existing infrastructure where adding enhanced sensing capabilities with an IoT solution is cost prohibitive. It is flexible: if a site is modified, the robots can be given a new route to monitor. It is also future proof as sensors can be easily upgraded as technology advances. Autonomous robot inspection is enabled by a software platform designed for full processing "at Edge" as a typical robotics inspection generates tens of gigabytes of data per hour. Data pre-processing, construction of detailed 3D temperature, vibration amplitude and gas concentration maps, identification of anomalies, their classification by criticality and communication to operators is done by computers at the facility. Time lapse analysis is used to process the data either in-situ, or remotely, depending on site connectivity. Reports are automatically generated indicating whether the site is functioning within normal working conditions, or an anomaly has been detected. The electronic reports contain the 3D model and are viewable on mobile devices providing the field engineer with detailed diagnostic data literally at their fingertips. The process builds and maintains the living digital twin without having to expend engineering hours to update 3D models. This process not only removes the need for people, which reduces cost and safety risk, but also improves reliability by increasing the available data set and reducing inspection intervals. By utilising data analytics and artificial intelligence, the anomalies detected can be risk ranked, prioritised and scheduled based on a latest acceptable completion date. This technology has been tested through a pilot program at an activated carbon plant in Wyoming during the summer of 2021. In this paper, we will present a case study of this pilot, covering installation, data acquisition and processing, as well as the key challenges and results.
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Pierson, Nicholas, Richard Clarke, and Oleg Mikhailov. "Transforming Asset Maintenance and Optimisation Through Autonomous Operations." In Offshore Technology Conference. OTC, 2022. http://dx.doi.org/10.4043/31847-ms.

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Abstract Autonomous Operations is about utilising the latest available fit-for-purpose technology in operations, maintenance, inspection, and asset optimisation. It involves collecting operating data such as visual, thermal, low- and high-frequency acoustic, FLIR (Forward Looking InfraRed), and gas-sensing through devices mounted on autonomous robots or drones, possibly mixed with stand-alone fixed devices. This "mobile sensor" approach is ideal for existing infrastructure where adding enhanced sensing capabilities with an IoT solution is cost prohibitive. It is flexible: if a site is modified, the robots can be given a new route to monitor. It is also future proof as sensors can be easily upgraded as technology advances. Autonomous robot inspection is enabled by a software platform designed for full processing "at Edge" as a typical robotics inspection generates tens of gigabytes of data per hour. Data pre-processing, construction of detailed 3D temperature, vibration amplitude and gas concentration maps, identification of anomalies, their classification by criticality and communication to operators is done by computers at the facility. Time lapse analysis is used to process the data either in-situ, or remotely, depending on site connectivity. Reports are automatically generated indicating whether the site is functioning within normal working conditions, or an anomaly has been detected. The electronic reports contain the 3D model and are viewable on mobile devices providing the field engineer with detailed diagnostic data literally at their fingertips. The process builds and maintains the living digital twin without having to expend engineering hours to update 3D models. This process not only removes the need for people, which reduces cost and safety risk, but also improves reliability by increasing the available data set and reducing inspection intervals. By utilising data analytics and artificial intelligence, the anomalies detected can be risk ranked, prioritised and scheduled based on a latest acceptable completion date. This technology has been tested through a pilot program at an activated carbon plant in Wyoming during the summer of 2021. In this paper, we will present a case study of this pilot, covering installation, data acquisition and processing, as well as the key challenges and results.
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Reports on the topic "Plant maintenance Data processing"

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Rosen, P. B. Kerman Photovoltaic Power Plant R&D data collection computer system operations and maintenance. Office of Scientific and Technical Information (OSTI), June 1994. http://dx.doi.org/10.2172/95580.

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Hill, Roger R., Geoffrey Taylor Klise, and John R. Balfour. Precursor Report of Data Needs and Recommended Practices for PV Plant Availability Operations and Maintenance Reporting. Office of Scientific and Technical Information (OSTI), January 2015. http://dx.doi.org/10.2172/1169447.

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Smyre, J. L., B. W. Moll, and A. L. King. Gamma radiological surveys of the Oak Ridge Reservation, Paducah Gaseous Diffusion Plant, and Portsmouth Gaseous Diffusion Plant, 1990-1993, and overview of data processing and analysis by the Environmental Restoration Remote Sensing Program, Fiscal Year 1995. Office of Scientific and Technical Information (OSTI), June 1996. http://dx.doi.org/10.2172/262973.

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Ron, Eliora, and Eugene Eugene Nester. Global functional genomics of plant cell transformation by agrobacterium. United States Department of Agriculture, March 2009. http://dx.doi.org/10.32747/2009.7695860.bard.

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The aim of this study was to carry out a global functional genomics analysis of plant cell transformation by Agrobacterium in order to define and characterize the physiology of Agrobacterium in the acidic environment of a wounded plant. We planed to study the proteome and transcriptome of Agrobacterium in response to a change in pH, from 7.2 to 5.5 and identify genes and circuits directly involved in this change. Bacteria-plant interactions involve a large number of global regulatory systems, which are essential for protection against new stressful conditions. The interaction of bacteria with their hosts has been previously studied by genetic-physiological methods. We wanted to make use of the new capabilities to study these interactions on a global scale, using transcription analysis (transcriptomics, microarrays) and proteomics (2D gel electrophoresis and mass spectrometry). The results provided extensive data on the functional genomics under conditions that partially mimic plant infection and – in addition - revealed some surprising and significant data. Thus, we identified the genes whose expression is modulated when Agrobacterium is grown under the acidic conditions found in the rhizosphere (pH 5.5), an essential environmental factor in Agrobacterium – plant interactions essential for induction of the virulence program by plant signal molecules. Among the 45 genes whose expression was significantly elevated, of special interest is the two-component chromosomally encoded system, ChvG/I which is involved in regulating acid inducible genes. A second exciting system under acid and ChvG/Icontrol is a secretion system for proteins, T6SS, encoded by 14 genes which appears to be important for Rhizobium leguminosarum nodule formation and nitrogen fixation and for virulence of Agrobacterium. The proteome analysis revealed that gamma aminobutyric acid (GABA), a metabolite secreted by wounded plants, induces the synthesis of an Agrobacterium lactonase which degrades the quorum sensing signal, N-acyl homoserine lactone (AHL), resulting in attenuation of virulence. In addition, through a transcriptomic analysis of Agrobacterium growing at the pH of the rhizosphere (pH=5.5), we demonstrated that salicylic acid (SA) a well-studied plant signal molecule important in plant defense, attenuates Agrobacterium virulence in two distinct ways - by down regulating the synthesis of the virulence (vir) genes required for the processing and transfer of the T-DNA and by inducing the same lactonase, which in turn degrades the AHL. Thus, GABA and SA with different molecular structures, induce the expression of these same genes. The identification of genes whose expression is modulated by conditions that mimic plant infection, as well as the identification of regulatory molecules that help control the early stages of infection, advance our understanding of this complex bacterial-plant interaction and has immediate potential applications to modify it. We expect that the data generated by our research will be used to develop novel strategies for the control of crown gall disease. Moreover, these results will also provide the basis for future biotechnological approaches that will use genetic manipulations to improve bacterial-plant interactions, leading to more efficient DNA transfer to recalcitrant plants and robust symbiosis. These advances will, in turn, contribute to plant protection by introducing genes for resistance against other bacteria, pests and environmental stress.
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Kull, Kathleen, Craig Young, Jennifer Haack-Gaynor, Lloyd Morrison, and Michael DeBacker. Problematic plant monitoring protocol for the Heartland Inventory and Monitoring Network: Narrative, version 2.0. National Park Service, May 2022. http://dx.doi.org/10.36967/nrr-2293355.

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Problematic species, which include invasive, exotic, and harmful species, fragment native ecosystems, displace native plants and animals, and alter ecosystem function. In National Parks, such species negatively affect park resources and visitor enjoyment by altering landscapes and fire regimes, reducing native plant and animal habitat, and increasing trail maintenance needs. Recognizing these challenges, Heartland Inventory and Monitoring (I&M) Network parks identified problematic plants as the highest-ranking vital sign across the network. Given the need to provide early detection of potential problematic plants (ProPs) and the size of network parks, the Heartland I&M Network opted to allocate available sampling effort to maximize the area searched. With this approach and the available sampling effort in mind, we developed realistic objectives for the ProP monitoring protocol. The monitoring objectives are: 1. Create a watch list of ProPs known to occur in network parks and a watch list of potential ProPs that may invade network parks in the future, and occasionally update these two lists as new information is made available. 2. Provide early detection monitoring for all ProPs on the watch lists. 3. Search at least 0.75% and up to 40% of the reference frame for ProP occurrences in each park. 4. Estimate/calculate and report the abundance and frequency of ProPs in each park. 5. To the extent possible, identify temporal changes in the distribution and abundance of ProPs known to occur in network parks. ProP watch lists are developed using the best available and most relevant state, regional, and national exotic plant lists. The lists are generated using the PriorityDB database. We designed the park reference frames (i.e., the area to be monitored) to focus on accessible natural and restored areas. The field methods vary for small parks and large parks, defined as parks with reference frames less than and greater than 350 acres (142 ha), respectively. For small parks, surveyors make three equidistant passes through polygon search units that are approximately 2-acres (0.8 ha) in size. For large parks, surveyors record each ProP encountered along 200-m or 400-m line search units. The cover of each ProP taxa encountered in search units is estimated using the following cover scale: 0 = 0, 1 = 0.1-0.9 m2, 2 = 1-9.9 m2, 3 = 10-49.9 m2, 4 = 50-99.9 m2, 5 = 100-499.9 m2, 6 = 499.9-999.9 m2, and 7 = 1,000-4,999.9 m2. The field data are managed in the FieldDB database. Monitoring is scheduled to revisit most parks every four years. The network will report the results to park managers and superintendents after completing ProP monitoring.
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Eshed-Williams, Leor, and Daniel Zilberman. Genetic and cellular networks regulating cell fate at the shoot apical meristem. United States Department of Agriculture, January 2014. http://dx.doi.org/10.32747/2014.7699862.bard.

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The shoot apical meristem establishes plant architecture by continuously producing new lateral organs such as leaves, axillary meristems and flowers throughout the plant life cycle. This unique capacity is achieved by a group of self-renewing pluripotent stem cells that give rise to founder cells, which can differentiate into multiple cell and tissue types in response to environmental and developmental cues. Cell fate specification at the shoot apical meristem is programmed primarily by transcription factors acting in a complex gene regulatory network. In this project we proposed to provide significant understanding of meristem maintenance and cell fate specification by studying four transcription factors acting at the meristem. Our original aim was to identify the direct target genes of WUS, STM, KNAT6 and CNA transcription factor in a genome wide scale and the manner by which they regulate their targets. Our goal was to integrate this data into a regulatory model of cell fate specification in the SAM and to identify key genes within the model for further study. We have generated transgenic plants carrying the four TF with two different tags and preformed chromatin Immunoprecipitation (ChIP) assay to identify the TF direct target genes. Due to unforeseen obstacles we have been delayed in achieving this aim but hope to accomplish it soon. Using the GR inducible system, genetic approach and transcriptome analysis [mRNA-seq] we provided a new look at meristem activity and its regulation of morphogenesis and phyllotaxy and propose a coherent framework for the role of many factors acting in meristem development and maintenance. We provided evidence for 3 different mechanisms for the regulation of WUS expression, DNA methylation, a second receptor pathway - the ERECTA receptor and the CNA TF that negatively regulates WUS expression in its own domain, the Organizing Center. We found that once the WUS expression level surpasses a certain threshold it alters cell identity at the periphery of the inflorescence meristem from floral meristem to carpel fate [FM]. When WUS expression highly elevated in the FM, the meristem turn into indeterminate. We showed that WUS activate cytokinine, inhibit auxin response and represses the genes required for root identity fate and that gradual increase in WUCHEL activity leads to gradual meristem enlargement that affect phyllotaxis. We also propose a model in which the direction of WUS domain expansion laterally or upward affects meristem structure differently. We preformed mRNA-seq on meristems with different size and structure followed by k-means clustering and identified groups of genes that are expressed in specific domains at the meristem. We will integrate this data with the ChIP-seq of the 4 TF to add another layer to the genetic network regulating meristem activity.
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Stern, David, and Gadi Schuster. Manipulation of Gene Expression in the Chloroplast. United States Department of Agriculture, September 2000. http://dx.doi.org/10.32747/2000.7575289.bard.

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The steady-state level of a given mRNA is determined by its rates of transcription and degradation. The stabilities of chloroplast mRNAs vary during plant development, in part regulating gene expression. Furthermore, the fitness of the organelle depends on its ability to destroy non-functional transcripts. In addition, there is a resurgent interest by the biotechnology community in chloroplast transformation due to the public concerns over pollen transmission of introduced traits or foreign proteins. Therefore, studies into basic gene expression mechanisms in the chloroplast will open the door to take advantage of these opportunities. This project was aimed at gaining mechanistic insights into mRNA processing and degradation in the chloroplast and to engineer transcripts of varying stability in Chlamydomonas reinhardtii cells. This research uncovered new and important information on chloroplast mRNA stability, processing, degradation and translation. In particular, the processing of the 3' untranslated regions of chloroplast mRNAs was shown to be important determinants in translation. The endonucleolytic site in the 3' untranslated region was characterized by site directed mutagensis. RNA polyadenylation has been characterized in the chloroplast of Chlamydomonas reinhardtii and chloroplast transformants carrying polyadenylated sequences were constructed and analyzed. Data obtained to date suggest that chloroplasts have gene regulatory mechanisms which are uniquely adapted to their post-endosymbiotic environment, including those that regulate RNA stability. An exciting point has been reached, because molecular genetic studies have defined critical RNA-protein interactions that participate in these processes. However, much remains to be learned about these multiple pathways, how they interact with each other, and how many nuclear genes are consecrated to overseeing them. Chlamydomonas is an ideal model system to extend our understanding of these areas, given its ease of manipulation and the existing knowledge base, some of which we have generated.
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Qi, Yan, Ryan Fries, Shambhu Saran Baral, and Pranesh Biswas. Evaluating the Costs and Benefits of Snow Fences in Illinois: Phase 2. Illinois Center for Transportation, November 2020. http://dx.doi.org/10.36501/0197-9191/20-020.

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Serving as a windbreak, properly sited and designed snow fences have been proven effective in mitigating the negative impacts of blowing snow. To achieve the best snow-control effects, the ideal locations for snow fences are usually outside the roadway right-of-way. Few efforts have been made to examine the economic efficiency of snow fences and explore ways to reward private landowners. The objective of this project was to develop methodologies for evaluation of the costs and benefits of snow fences in Illinois and identify ways to encourage private landowners’ participation in the snow fence program while keeping it cost-effective. The researchers conducted a literature review as well as agency and landowner surveys. They also acquired crash data, snow fence and blowing snow segment inventory data, and blowing snow removal expenditure data as well as performed benefit-cost analyses of three types of snow fences following Federal Highway Administration guides. The survey results suggested that standing corn rows (SCRs) and structural snow fences (SSFs) were the least intrusive options for landowners and living snow fences (LSFs) with trees were the most intrusive. Some concerns related to LSFs could be reduced by allowing landowners to play a role in the design and plant-selection process. The crash data indicated that no fatal and severe crashes occurred at snow fence segments, while several fatal and severe crashes occurred at blowing snow segments during 2012–2016. The results of the benefit-cost analyses showed that the benefit-cost ratios for LSFs and SSFs are comparable. However, LSFs are favorable over SSFs because little maintenance is needed after the plants are mature. Although SCRs have the highest benefit-cost ratio, the need to renew the agency-landowner agreement annually and the alternating of crops planted may limit their snow-control effectiveness and large-scale implementation. A tool was developed using MS Excel to facilitate the benefit-cost analysis of snow fences.
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Petrie, John, Yan Qi, Mark Cornwell, Md Al Adib Sarker, Pranesh Biswas, Sen Du, and Xianming Shi. Design of Living Barriers to Reduce the Impacts of Snowdrifts on Illinois Freeways. Illinois Center for Transportation, November 2020. http://dx.doi.org/10.36501/0197-9191/20-019.

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Blowing snow accounts for a large part of Illinois Department of Transportation’s total winter maintenance expenditures. This project aims to develop recommendations on the design and placement of living snow fences (LSFs) to minimize snowdrift on Illinois highways. The research team examined historical IDOT data for resource expenditures, conducted a literature review and survey of northern agencies, developed and validated a numerical model, field tested selected LSFs, and used a model to assist LSF design. Field testing revealed that the proper snow fence setback distance should consider the local prevailing winter weather conditions, and snow fences within the right-of-way could still be beneficial to agencies. A series of numerical simulations of flow around porous fences were performed using Flow-3D, a computational fluid dynamics software. The results of the simulations of the validated model were employed to develop design guidelines for siting LSFs on flat terrain and for those with mild slopes (< 15° from horizontal). Guidance is provided for determining fence setback, wind characteristics, fence orientation, as well as fence height and porosity. Fences comprised of multiple rows are also addressed. For sites with embankments with steeper slopes, guidelines are provided that include a fence at the base and one or more fence on the embankment. The design procedure can use the available right-of-way at a site to determine the appropriate fence characteristics (e.g., height and porosity) to prevent snow deposition on the road. The procedure developed in this work provides an alternative that uses available setback to design the fence. This approach does not consider snow transport over an entire season and may be less effective in years with several large snowfall events, very large single events, or a sequence of small events with little snowmelt in between. However, this procedure is expected to be effective for more frequent snowfall events such as those that occurred over the field-monitoring period. Recommendations were made to facilitate the implementation of research results by IDOT. The recommendations include a proposed process flow for establishing LSFs for Illinois highways, LSF siting and design guidelines (along with a list of suitable plant species for LSFs), as well as other implementation considerations and identified research needs.
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10

Fluhr, Robert, and Volker Brendel. Harnessing the genetic diversity engendered by alternative gene splicing. United States Department of Agriculture, December 2005. http://dx.doi.org/10.32747/2005.7696517.bard.

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Our original objectives were to assess the unexplored dimension of alternative splicing as a source of genetic variation. In particular, we sought to initially establish an alternative splicing database for Arabidopsis, the only plant for which a near-complete genome has been assembled. Our goal was to then use the database, in part, to advance plant gene prediction programs that are currently a limiting factor in annotating genomic sequence data and thus will facilitate the exploitation of the ever increasing quantity of raw genomic data accumulating for plants. Additionally, the database was to be used to generate probes for establishing high-throughput alternative transcriptome analysis in the form of a splicing-specific oligonucleotide microarray. We achieved the first goal and established a database and web site termed Alternative Splicing In Plants (ASIP, http://www.plantgdb.org/ASIP/). We also thoroughly reviewed the extent of alternative splicing in plants (Arabidopsis and rice) and proposed mechanisms for transcript processing. We noted that the repertoire of plant alternative splicing differs from that encountered in animals. For example, intron retention turned out to be the major type. This surprising development was proven by direct RNA isolation techniques. We further analyzed EST databases available from many plants and developed a process to assess their alternative splicing rate. Our results show that the lager genome-sized plant species have enhanced rates of alternative splicing. We did advance gene prediction accuracy in plants by incorporating scoring for non-canonical introns. Our data and programs are now being used in the continuing annotation of plant genomes of agronomic importance, including corn, soybean, and tomato. Based on the gene annotation data developed in the early part of the project, it turned out that specific probes for different exons could not be scaled up to a large array because no uniform hybridization conditions could be found. Therefore, we modified our original objective to design and produce an oligonucleotide microarray for probing alternative splicing and realized that it may be reasonable to investigate the extent of alternative splicing using novel commercial whole genome arrays. This possibility was directly examined by establishing algorithms for the analysis of such arrays. The predictive value of the algorithms was then shown by isolation and verification of alternative splicing predictions from the published whole genome array databases. The BARD-funded work provides a significant advance in understanding the extent and possible roles of alternative splicing in plants as well as a foundation for advances in computational gene prediction.
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