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1
Marshall, David F. "Language Maintenance and Revival." Annual Review of Applied Linguistics 14 (March 1994): 20–33. http://dx.doi.org/10.1017/s0267190500002798.
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Johann Gottfreid Herder illustrated how problematic language maintenance predictions can be with his prediction in his essay, On the Origin of Languages, stating that Hungarian would briefly disappear from the surface of the earth as if it had never existed. With over 10 million speakers today in Hungary, another 4 million outside the nation, and a growing population (Hungarians in the Outside World 1993), Herder's prediction remains hyperbolic, yet it illustrates how dangerous such predictions about language maintenance can be. Hungarian, as with other languages, has been maintained because of forces operating in the unique history of the nation.
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Xu, Peng, Rengkui Liu, Quanxin Sun, and Futian Wang. "A Novel Short-Range Prediction Model for Railway Track Irregularity." Discrete Dynamics in Nature and Society 2012 (2012): 1–12. http://dx.doi.org/10.1155/2012/591490.
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In recent years, with axle loads, train loads, transport volume, and travel speed constantly increasing and railway network steadily lengthening, shortcomings of current maintenance strategies are getting to be noticed from an economical and safety perspective. To overcome the shortcomings, permanent-of-way departments throughout the world have given a considerable attention to an ideal maintenance strategy which is to carry out appropriate maintenances just in time on track locations really requiring maintenance. This strategy is simplified as the condition-based maintenance (CBM) which has attracted attentions of engineers of many industries in the recent 70 years. To implement CBM for track irregularity, there are many issues which need to be addressed. One of them focuses on predicting track irregularity of each day in a future short period. In this paper, based on track irregularity evolution characteristics, a Short-Range Prediction Model was developed to this aim and is abbreviated to TI-SRPM. Performance analysis results for TI-SRPM illustrate that track irregularity amplitude predictions on sampling points by TI-SRPM are very close to their measurements by Track Geometry Car.
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Nansamba, Salmah, and Hadi Harb. "Developing a Neural Network Based Fault Prediction Tool for a Solar Power Plant in Uganda." Transactions on Machine Learning and Artificial Intelligence 10, no. 6 (December 28, 2022): 52–70. http://dx.doi.org/10.14738/tmlai.106.13645.
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Solar photovoltaic (PV) systems are one of the fastest growing renewable energy technologies and plenty of research has been and continues to be carried out in this domain. Maximization of solar PV power plant production, efficiency and return on investment can only be achieved by having adequate and effective maintenance systems in place. Of the various maintenance schemes, predictive maintenance is popular for its effectiveness and minimization of resource wastage. Maintenance activities are scheduled based on the real time condition of the system with priority being given to the system components with the highest likelihood of failure. A good predictive maintenance system is based on the premise of being able to anticipate faults before they occur. In this study therefore, a fault prediction tool for a solar plant in Uganda is proposed. The hybrid tool is developed using both feed forward and long short term memory neural networks for power prediction, in conjunction with a mean chart statistical process control tool for final fault prediction. Results from the study demonstrate that the feed forward and long short term memory neural network modules of the proposed tool attain mean absolute errors of 4.2% and 6.9% respectively for power production predictions. The fault prediction capability of the tool is tested under both normal and abnormal operating conditions. Results show that the tool satisfactorily discriminates against the fault and non-fault conditions thereby achieving successful solar PV system fault prediction.
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Kang, Ziqiu, Cagatay Catal, and Bedir Tekinerdogan. "Remaining Useful Life (RUL) Prediction of Equipment in Production Lines Using Artificial Neural Networks." Sensors 21, no. 3 (January 30, 2021): 932. http://dx.doi.org/10.3390/s21030932.
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Predictive maintenance of production lines is important to early detect possible defects and thus identify and apply the required maintenance activities to avoid possible breakdowns. An important concern in predictive maintenance is the prediction of remaining useful life (RUL), which is an estimate of the number of remaining years that a component in a production line is estimated to be able to function in accordance with its intended purpose before warranting replacement. In this study, we propose a novel machine learning-based approach for automating the prediction of the failure of equipment in continuous production lines. The proposed model applies normalization and principle component analysis during the pre-processing stage, utilizes interpolation, uses grid search for parameter optimization, and is built with multilayer perceptron neural network (MLP) machine learning algorithm. We have evaluated the approach using a case study research to predict the RUL of engines on NASA turbo engine datasets. Experimental results demonstrate that the performance of our proposed model is effective in predicting the RUL of turbo engines and likewise substantially enhances predictive maintenance results.
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Fitra Azyus, Adryan, Sastra Kusuma Wijaya, and Mohd Naved. "Determining RUL Predictive Maintenance on Aircraft Engines Using GRU." Journal of Mechanical, Civil and Industrial Engineering 3, no. 3 (December 11, 2022): 79–84. http://dx.doi.org/10.32996/jmcie.2022.3.3.10.
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Prognostic and health management (PHM) in the aviation industry is expanding because of its effect on economic and human safety. Advanced maintenance shall be applied to this industry to inform aircraft engine conditions. PdM (Predictive Maintenance) is an advanced maintenance technique that can be applied to the aviation industry because of its high-precision prediction. Combining PdM as a technique to calculate the RUL (Remaining Useful Lifetime ) and ML (Machine Learning) as a tool to make high-accuracy predictions is mixed together that accurately forecasts the state of aircraft machine condition and on the best time to get the maintenance or service. In this work, we use the NASA Commercial Modular Aero-Propulsion System Simulation (C-MAPSS) data set. This work proposes GRU to determine RUL on aircraft engines to implement a Predictive maintenance strategy. For the training parameters tested in this study, we used a batch size of 512, a learning rate with Adam optimizer of 0.001, then epochs of 200. The essence of the results of this experiment is to obtain a new method with a simpler calculation process and the epoch value and a faster prediction process compared to other methods used, and the results obtained can approach the original value from an economic point of view and the RUL prediction process using the GRU.
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D., Ganga, and Ramachandran V. "Adaptive prediction model for effective electrical machine maintenance." Journal of Quality in Maintenance Engineering 26, no. 1 (April 18, 2019): 166–80. http://dx.doi.org/10.1108/jqme-12-2017-0087.
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Purpose The purpose of this paper is to propose an optimal predictive model for the short-term forecast of real-time non-stationary machine variables by combining time series prediction with adaptive algorithms to minimize the error and to improve the prediction accuracy. Design/methodology/approach The proposed model is applied for prediction of speed and controller set point of three-phase induction motor operating on closed loop speed control with AC drive and PI controller. At Stage 1, the trend of the machine variables has been extracted and added to auto-regressive moving average (ARMA) time series prediction. ARMA prediction has been carried out using different combinations of AR and MA methods in order to make prediction with less Mean Squared Error (MSE). Findings The prediction error indicates the inadequacy of the model to estimate the data characteristics, which has been resolved at the subsequent stage by cascading an adaptive least mean square finite impulse response filter to the time series model. The adaptive filter receives the predicted output including training data and iteratively adjusts its coefficients for zero error convergence. Research limitations/implications The componentized data prediction based on time series and cascade adaptive filter algorithm decomposes the non-stationary data characteristics for predictive maintenance. Evaluation of the model with different combination of time series algorithms and parameter settings of adaptive filter has been carried out to illustrate the performance of the prediction model. This prediction accuracy is compared with existing linear adaptive filter prediction using MSE as comparison index. The wide margin in the MSE values substantiates the prediction efficiency of the proposed model for machine data. Originality/value This model predicts the dynamic machine data with component decomposition at high accuracy, which enables to interpret the system response under dynamic conditions efficiently.
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Tong, Guoqiang, Xinbo Qian, and Yilai Liu. "Prognostics and Predictive Maintenance Optimization Based on Combination BP-RBF-GRNN Neural Network Model and Proportional Hazard Model." Journal of Sensors 2022 (April 29, 2022): 1–17. http://dx.doi.org/10.1155/2022/8655669.
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Owning to the advantage of keeping the operating environment safe, high reliability, and low production cost, predictive maintenance has been widely used in industry and academia. Predictive maintenance based on degeneration state mainly studies the degeneration prediction. However, on account of the error of the sensor and human, condition monitoring data may not directly reflect the true degeneration. The degeneration model with dynamic explanatory covariates which is named as proportional hazard model is proposed to deal with the semi-observed monitoring condition. And the degeneration prediction mainly adopts a single prediction model, which leads to low prediction accuracy. A combination forecasting model can effectively solve the above problem. Compared to the traditional prediction method, the neural network model can use the “black box” characteristic to indirectly construct the degeneration model without complex mathematical derivation. Therefore, we propose a combination BP-RBF-GRNN neural network model which is applied to improve the degeneration prediction with dynamic covariate. Based on the above two aspects, a predictive maintenance optimization framework based on the proportional hazard model and BP-RBF-GRNN neural network model is proposed to improve maintenance efficiency and reduce maintenance costs. The simulation results of thrust ball bearing show that the proposed method can effectively improve the degeneration prediction accuracy and reduce the maintenance cost rate to a certain extent.
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Rodrigues, Joao, Jose Torres Farinha, and Antonio Marques Cardoso. "Predictive Maintenance Tools – A Global Survey." WSEAS TRANSACTIONS ON SYSTEMS AND CONTROL 16 (January 22, 2021): 96–109. http://dx.doi.org/10.37394/23203.2021.16.7.
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The importance given to the maintenance in the industrial world has grown over time, with new methods, new procedures and new challenges, due to the availability of new technologies. This paper focus on a global survey about predictive maintenance tools that support predictive maintenance, from the time series and decision trees until Artificial Intelligence. The approach of the several tools that can help the prediction is holistic, because new tools do not eliminate the importance of the old ones: they are complimentary and each new tool that is developed add potential for a better prediction. Additionally, it must be emphasized that some tools, that seem new are, in practice, old tools with new and powerful computational devices, assuming a new and strategic importance nowadays.
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Gibiec, Mariusz. "Prediction of Machines Health with Application of an Intelligent Approach – a Mining Machinery Case Study." Key Engineering Materials 293-294 (September 2005): 661–68. http://dx.doi.org/10.4028/www.scientific.net/kem.293-294.661.
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Machine field services depend on sensor-driven management systems that provide alerts, alarms and indicators. At the moment the alarm is sounded, it’s sometimes too late to prevent the failure. There is no alert provided that looks at degradation over time. If we could monitor degradation, then we would forecast upcoming situations, and perform maintenance tasks when necessary. In our research we chose to focus on intelligent maintenance system, which is defined as the prediction and forecast of equipment performance. Predictive maintenance, on the other hand, focuses on machine performance features. Data come from two sources: sensors mounted on the machine to gather the machine feature information, and information from the entire manufacturing system, including machine productivity, past history and trending. By correlating data from these sources — current and historical — predictions can be made about future performance. In this article case study of coal mining machinery health prediction is presented. Health of water pumping unit was considered. Such units placed in old mine shafts are crucial to avoid flooding working ones. As an effect of predictive maintenance it can be possible to improve safety and reduce costs incurred from accidents.
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Zhu, Ya Hong, Ji Ping Cao, Wen Xia Sun, Yang Tao Fan, and Zhi Hui Zhao. "Demand Forecasting Model Based on Equipment Maintenance Resources in Virtual Warehousing." Applied Mechanics and Materials 556-562 (May 2014): 5442–49. http://dx.doi.org/10.4028/www.scientific.net/amm.556-562.5442.
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Based on the theory of virtual warehousing, the optimization system for equipment maintenance resources in virtual warehousing is established for the security task of equipment maintenance resources. According to the prediction problems on the spare parts requirements for equipment maintenance in this system, the demand forecasting model, based on the combination of rough sets and grey prediction, is adopted. The results of simulation experiment show that this method applied in equipment maintenance spare resources prediction is reliable and with accurate information. While, the relative error and absolute error of the predictive value and practical value are very small, which shows the prediction model is of high precision for the accurate effect prediction. As a result, this model and algorithum is proved to be effective to provide theoretical and practical support for equipment maintenance spare resources in information warfare.
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Song, Weixing, Jingjing Wu, Jianshe Kang, and Jun Zhang. "Research on maintenance spare parts requirement prediction based on LSTM recurrent neural network." Open Physics 19, no. 1 (January 1, 2021): 618–27. http://dx.doi.org/10.1515/phys-2021-0072.
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Abstract The aim of this study was to improve the low accuracy of equipment spare parts requirement predicting, which affects the quality and efficiency of maintenance support, based on the summary and analysis of the existing spare parts requirement predicting research. This article introduces the current latest popular long short-term memory (LSTM) algorithm which has the best effect on time series data processing to equipment spare parts requirement predicting, according to the time series characteristics of spare parts consumption data. A method for predicting the requirement for maintenance spare parts based on the LSTM recurrent neural network is proposed, and the network structure is designed in detail, the realization of network training and network prediction is given. The advantages of particle swarm algorithm are introduced to optimize the network parameters, and actual data of three types of equipment spare parts consumption are used for experiments. The performance comparison of predictive models such as BP neural network, generalized regression neural network, wavelet neural network, and squeeze-and-excitation network prove that the new method is effective and provides an effective method for scientifically predicting the requirement for maintenance spare parts and improving the quality of equipment maintenance.
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Yang, Jia, Yongkui Sun, Yuan Cao, and Xiaoxi Hu. "Predictive Maintenance for Switch Machine Based on Digital Twins." Information 12, no. 11 (November 22, 2021): 485. http://dx.doi.org/10.3390/info12110485.
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As a unique device of railway networks, the normal operation of switch machines involves railway safe and efficient operation. Predictive maintenance becomes the focus of the switch machine. Aiming at the low accuracy of the prediction state and the difficulty in state visualization, the paper proposes a predictive maintenance model for switch machines based on Digital Twins (DT). It constructs a DT model for the switch machine, which contains a behavior model and a rule model. The behavior model is a high-fidelity visual model. The rule model is a high-precision prediction model, which is combined with long short-term memory (LSTM) and autoregressive Integrated Moving Average model (ARIMA). Experiment results show that the model can be more intuitive with higher prediction accuracy and better applicability. The proposed DT approach is potentially practical, providing a promising idea for switching machines in predictive maintenance.
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Segovia-Muñoz, D., X. Serrano-Guerrero, and A. Barragán-Escandon. "Predictive maintenance in LED street lighting controlled with telemanagement system to improve current fault detection procedures using software tools." Renewable Energy and Power Quality Journal 20 (September 2022): 379–86. http://dx.doi.org/10.24084/repqj20.318.
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Predicting the lifetime of LED light sources becomes quite challenging because the time to failure is long. The LM-80 and TM-21 methods are the main used by companies to establish the product lifetime. Accurate the RUL prediction can facilitate predictive maintenance. Predictive maintenance allows estimating when a failure will occur. In this context, the maintenance can be planned in advance, eliminating unplanned outage and maximizing the useful life of the equipment. In this work, the LM-80 and TM-21 methods are used for the acquisition and extrapolation of luminous flux data, wich are entered into an algorithm developed from an exponential degradation model. With the result obtained, it is possible to establish actions that allow predictive maintenance in LED street lighting controlled by a remote management system and achieve a longer service life.
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Nordal, Helge, and Idriss El-Thalji. "Lifetime Benefit Analysis of Intelligent Maintenance: Simulation Modeling Approach and Industrial Case Study." Applied Sciences 11, no. 8 (April 13, 2021): 3487. http://dx.doi.org/10.3390/app11083487.
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The introduction of Industry 4.0 is expected to revolutionize current maintenance practices by reaching new levels of predictive (detection, diagnosis, and prognosis processes) and prescriptive maintenance analytics. In general, the new maintenance paradigms (predictive and prescriptive) are often difficult to justify because of their multiple inherent trade-offs and hidden systems causalities. The prediction models, in the literature, can be considered as a “black box” that is missing the links between input data, analysis, and final predictions, which makes the industrial adaptability to such models almost impossible. It is also missing enable modeling deterioration based on loading, or considering technical specifications related to detection, diagnosis, and prognosis, which are all decisive for intelligent maintenance purposes. The purpose and scientific contribution of this paper is to present a novel simulation model that enables estimating the lifetime benefits of an industrial asset when an intelligent maintenance management system is utilized as mixed maintenance strategies and the predictive maintenance (PdM) is leveraged into opportunistic intervals. The multi-method simulation modeling approach combining agent-based modeling with system dynamics is applied with a purposefully selected case study to conceptualize and validate the simulation model. Three maintenance strategies (preventive, corrective, and intelligent) and five different scenarios (case study data, manipulated case study data, offshore and onshore reliability data handbook (OREDA) database, physics-based data, and hybrid) are modeled and simulated for a time period of 20 years (175,200 h). Intelligent maintenance is defined as PdM leveraged in opportunistic maintenance intervals. The results clearly demonstrate the possible lifetime benefits of implementing an intelligent maintenance system into the case study as it enhanced the operational availability by 0.268% and reduced corrective maintenance workload by 459 h or 11%. The multi-method simulation model leverages and shows the effect of the physics-based data (deterioration curves), loading profiles, and detection and prediction levels. It is concluded that implementing intelligent maintenance without an effective predictive horizon of the associated PdM and effective frequency of opportunistic maintenance intervals, does not guarantee the gain of its lifetime benefits. Moreover, the case study maintenance data shall be collected in a complete (no missing data) and more accurate manner (use hours instead of date only) and used to continuously upgrade the failure rates and maintenance times.
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Chen, Lei, Lijun Wei, Wenlong Li, Junhui Wang, and Dongyang Han. "Fault Prediction of Mechanical Equipment Based on Hilbert–Full-Vector Spectrum and TCDAN." Applied Sciences 13, no. 8 (April 7, 2023): 4655. http://dx.doi.org/10.3390/app13084655.
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To solve the problem of “under-maintenance” and “over-maintenance” in the daily maintenance of equipment, the predictive maintenance method based on the running state of equipment has shown great advantages, and fault prediction is an important part of predictive maintenance. First, the spectrum information of the equipment is extracted by the Hilbert–full-vector spectrum as the input of fault prediction. Compared with the traditional spectrum, this spectrum information fuses the signals of two sensors in the same section of the device, which can reflect the actual operational state of the device more comprehensively. Then, the temporal convolutional network is used to predict the amplitudes of different feature frequencies, and the double-layer attention mechanism is introduced to mine the correlation between the corresponding amplitudes of different feature frequencies and between the data at different historical moments, to highlight the more important influencing factors. In this way, the prediction accuracy of the model for the amplitude corresponding to the feature frequency of concern is improved. Finally, experimental verification is carried out on the XJTU-SY dataset. The results show that the TCDAN model proposed in this paper is significantly superior to TCN, GRU, BiLSTM, and LSTM, which can provide a more effective decision-making basis for the predictive maintenance of equipment.
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Mbembati, Hadija, Kwame Ibwe, and Baraka Maiseli. "Maintenance Automation Architecture and Electrical Equipment Fault Prediction Method in Tanzania Secondary Distribution Networks." Tanzania Journal of Science 47, no. 3 (August 15, 2021): 1138–53. http://dx.doi.org/10.4314/tjs.v47i3.23.
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Distribution networks remain the most maintenance-intensive parts of power systems. The implementation of maintenance automation and prediction of equipment fault can enhance system reliability while reducing the overall costs. In Tanzania, however, maintenance automation has not been deployed in secondary distribution networks (SDNs). Instead, traditional methods are used for condition prediction and fault identification of power assets (transformers and power lines). These (manual) methods are costly and time-consuming, and may introduce human-related errors. Motivated by these challenges, this work introduces maintenance automation into the network architecture by implementing effective maintenance and fault identification methods. The proposed method adopts machine learning techniques to develop a novel system architecture for maintenance automation in the SDN. Experimental results showed that different transformer prediction methods, namely support vector machine, kernel support vector machine, and multi-layer artificial neural network, give performance values of 96.72%, 97.50%, and 97.53%, respectively. Furthermore, oil based performance analysis was done to compare the existing methods with the proposed method. Simulation results showed that the proposed method can accurately identify up to ten transformer abnormalities. These results suggest that the proposed system may be integrated into a maintenance scheduling platform to reduce unplanned maintenance outages and human maintenance-related errors.
Keywords: Predictive maintenance; fault identification; fault prediction; maintenance automation; secondary electrical distribution network
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Onawumi, A. S., A. Aremu, O. A. Ajiboso, O. O. Agboola, T. M. A. Olayanju, and C. O. Osueke. "Development of strategic maintenance prediction model for critical equipment maintenance." Materials Today: Proceedings 44 (2021): 2820–27. http://dx.doi.org/10.1016/j.matpr.2020.12.1163.
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Gómez-Pau, Álvaro, Jordi-Roger Riba, and Manuel Moreno-Eguilaz. "Time Series RUL Estimation of Medium Voltage Connectors to Ease Predictive Maintenance Plans." Applied Sciences 10, no. 24 (December 17, 2020): 9041. http://dx.doi.org/10.3390/app10249041.
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The ageing process of medium voltage power connectors can lead to important power system faults. An on-line prediction of the remaining useful life (RUL) is a convenient strategy to prevent such failures, thus easing the application of predictive maintenance plans. The electrical resistance of the connector is the most widely used health indicator for condition monitoring and RUL prediction, even though its measurement is a challenging task because of its low value, which typically falls in the range of a few micro-ohms. At the present time, the RUL of power connectors is not estimated, since their electrical parameters are not monitored because medium voltage connectors are considered cheap and secondary devices in power systems, despite they play a critical role, so their failure can lead to important power flow interruptions with the consequent safety risks and economic losses. Therefore, there is an imperious need to develop on-line RUL prediction strategies. This paper develops an on-line method to solve this issue, by predicting the RUL of medium voltage connectors based on the degradation trajectory of the electrical resistance, which is characterized by analyzing the electrical resistance time series data by means of the autoregressive integrated moving average (ARIMA) method. The approach proposed in this paper allows applying predictive maintenance plans, since the RUL enables determining when the power connector must be replaced by a new one. Experimental results obtained from several connectors illustrate the feasibility and accuracy of the proposed approach for an on-line RUL prediction of power connectors.
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Karimpour, Mostafa, Lalith Hitihamillage, Najwa Elkhoury, Sara Moridpour, and Reyhaneh Hesami. "Fuzzy Approach in Rail Track Degradation Prediction." Journal of Advanced Transportation 2018 (2018): 1–7. http://dx.doi.org/10.1155/2018/3096190.
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Rail transport authorities around the world have been facing a significant challenge when predicting rail infrastructure maintenance work. With the restrictions on financial support, the rail transport authorities are in pursuit of improved modern methods, which can provide a precise prediction of rail maintenance timeframe. The expectation from such a method is to develop models to minimise the human error that is strongly related to manual prediction. Such models will help rail transport authorities in understanding how the track degradation occurs at different conditions (e.g., rail type, rail profile) over time. They need a well-structured technique to identify the precise time when rail tracks fail to minimise the maintenance cost/time. The rail track characteristics that have been collected over the years will be used in developing a degradation prediction model for rail tracks. Since these data have been collected in large volumes and the data collection is done both electronically and manually, it is possible to have some errors. Sometimes these errors make it impossible to use the data in prediction model development. An accurate model can play a key role in the estimation of the long-term behaviour of rail tracks. Accurate models can increase the efficiency of maintenance activities and decrease the cost of maintenance in long-term. In this research, a short review of rail track degradation prediction models has been discussed before estimating rail track degradation for the curves and straight sections of Melbourne tram track system using Adaptive Network-based Fuzzy Inference System (ANFIS) model. The results from the developed model show that it is capable of predicting the gauge values with R2 of 0.6 and 0.78 for curves and straights, respectively.
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Yang, Dezhen, Yidan Cui, Quan Xia, Fusheng Jiang, Yi Ren, Bo Sun, Qiang Feng, Zili Wang, and Chao Yang. "A Digital Twin-Driven Life Prediction Method of Lithium-Ion Batteries Based on Adaptive Model Evolution." Materials 15, no. 9 (May 6, 2022): 3331. http://dx.doi.org/10.3390/ma15093331.
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Accurate life prediction and reliability evaluation of lithium-ion batteries are of great significance for predictive maintenance. In the whole life cycle of a battery, the accurate description of the dynamic and stochastic characteristics of life has always been a key problem. In this paper, the concept of the digital twin is introduced, and a digital twin for reliability based on remaining useful cycle life prediction is proposed for lithium-ion batteries. The capacity degradation model, stochastic degradation model, life prediction, and reliability evaluation model are established to describe the randomness of battery degradation and the dispersion of the life of multiple cells. Based on the Bayesian algorithm, an adaptive evolution method for the model of the digital twin is proposed to improve prediction accuracy, followed by experimental verification. Finally, the life prediction, reliability evaluation, and predictive maintenance of the battery based on the digital twin are implemented. The results show the digital twin for reliability has good accuracy in the whole life cycle. The error can be controlled at about 5% with the adaptive evolution algorithm. For battery L1 and L6 in this case, predictive maintenance costs are expected to decrease by 62.0% and 52.5%, respectively.
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MIZUTANI, YOSHIKATSU. "Trial of warfarin maintenance dose prediction." Rinsho yakuri/Japanese Journal of Clinical Pharmacology and Therapeutics 26, no. 1 (1995): 177–78. http://dx.doi.org/10.3999/jscpt.26.177.
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Langeron, Yves, Mitra Fouladirad, and Antoine Grall. "Controlled systems, failure prediction and maintenance." IFAC-PapersOnLine 49, no. 12 (2016): 805–8. http://dx.doi.org/10.1016/j.ifacol.2016.07.873.
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Maktoubian, Jamal, Mohammad Sadegh Taskhiri, and Paul Turner. "Intelligent Predictive Maintenance (IPdM) in Forestry: A Review of Challenges and Opportunities." Forests 12, no. 11 (October 29, 2021): 1495. http://dx.doi.org/10.3390/f12111495.
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The feasibility of reliably generating bioenergy from forest biomass waste is intimately linked to supply chain and production processing costs. These costs are, at least in part, directly related to assumptions about the reliability and cost-efficiency of the machinery used along the forestry bioenergy supply chain. Although mechanization in forestry operations has advanced in the last 20 years, it is evident that challenges remain in relation to production capability, standardization of wood quality, and supply guarantee from forestry resources because of the age and reliability of the machinery. An important component in sustainable bioenergy from biomass supply chains will be confidence in consistent production costs linked to guarantees about harvest and haulage machinery reliability. In this context, this paper examines the issue of machinery maintenance and advances in machine learning and big data analysis that are contributing to improved intelligent prediction that is aiding supply chain reliability in bioenergy from woody biomass. The concept of “Industry 4.0” refers to the integration of numerous technologies and business processes that are transforming many aspects of conventional industries. In the realm of machinery maintenance, the dramatic increase in the capacity to dynamically collect, collate, and analyze data inputs including maintenance archive data, sensor-based monitoring, and external environmental and contextual variables. Big data analytics offers the potential to enhance the identification and prediction of maintenance (PdM) requirements. Given that estimates of costs associated with machinery maintenance vary between 20% and 60% of the overall costs, the need to find ways to better mitigate these costs is important. While PdM has been shown to help, it is noticeable that to-date there has been limited assessment of the impacts of external factors such as weather condition, operator experiences and/or operator fatigue on maintenance costs, and in turn the accuracy of maintenance predictions. While some researchers argue these data are captured by sensors on machinery components, this remains to be proven and efforts to enhance weighted calibrations for these external factors may further contribute to improving the prediction accuracy of remaining useful life (RUL) of machinery. This paper reviews and analyzes underlying assumptions embedded in different types of data used in maintenance regimes and assesses their quality and their current utility for predictive maintenance in forestry. The paper also describes an approach to building ‘intelligent’ predictive maintenance for forestry by incorporating external variables data into the computational maintenance model. Based on these insights, the paper presents a model for an intelligent predictive maintenance system (IPdM) for forestry and a method for its implementation and evaluation in the field.
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Peng, Cheng, Yufeng Chen, Qing Chen, Zhaohui Tang, Lingling Li, and Weihua Gui. "A Remaining Useful Life Prognosis of Turbofan Engine Using Temporal and Spatial Feature Fusion." Sensors 21, no. 2 (January 8, 2021): 418. http://dx.doi.org/10.3390/s21020418.
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The prognosis of the remaining useful life (RUL) of turbofan engine provides an important basis for predictive maintenance and remanufacturing, and plays a major role in reducing failure rate and maintenance costs. The main problem of traditional methods based on the single neural network of shallow machine learning is the RUL prognosis based on single feature extraction, and the prediction accuracy is generally not high, a method for predicting RUL based on the combination of one-dimensional convolutional neural networks with full convolutional layer (1-FCLCNN) and long short-term memory (LSTM) is proposed. In this method, LSTM and 1- FCLCNN are adopted to extract temporal and spatial features of FD001 andFD003 datasets generated by turbofan engine respectively. The fusion of these two kinds of features is for the input of the next convolutional neural networks (CNN) to obtain the target RUL. Compared with the currently popular RUL prediction models, the results show that the model proposed has higher prediction accuracy than other models in RUL prediction. The final evaluation index also shows the effectiveness and superiority of the model.
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Peng, Cheng, Yufeng Chen, Qing Chen, Zhaohui Tang, Lingling Li, and Weihua Gui. "A Remaining Useful Life Prognosis of Turbofan Engine Using Temporal and Spatial Feature Fusion." Sensors 21, no. 2 (January 8, 2021): 418. http://dx.doi.org/10.3390/s21020418.
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The prognosis of the remaining useful life (RUL) of turbofan engine provides an important basis for predictive maintenance and remanufacturing, and plays a major role in reducing failure rate and maintenance costs. The main problem of traditional methods based on the single neural network of shallow machine learning is the RUL prognosis based on single feature extraction, and the prediction accuracy is generally not high, a method for predicting RUL based on the combination of one-dimensional convolutional neural networks with full convolutional layer (1-FCLCNN) and long short-term memory (LSTM) is proposed. In this method, LSTM and 1- FCLCNN are adopted to extract temporal and spatial features of FD001 andFD003 datasets generated by turbofan engine respectively. The fusion of these two kinds of features is for the input of the next convolutional neural networks (CNN) to obtain the target RUL. Compared with the currently popular RUL prediction models, the results show that the model proposed has higher prediction accuracy than other models in RUL prediction. The final evaluation index also shows the effectiveness and superiority of the model.
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Liu, Li, Zhihui Zhang, Zhijian Qu, and Adrian Bell. "Remaining Useful Life Prediction for a Catenary, Utilizing Bayesian Optimization of Stacking." Electronics 12, no. 7 (April 6, 2023): 1744. http://dx.doi.org/10.3390/electronics12071744.
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This article addresses the problem that the remaining useful life (RUL) prediction accuracy for a high-speed rail catenary is not accurate enough, leading to costly and time-consuming periodic planned and reactive maintenance costs. A new method for predicting the RUL of a catenary is proposed based on the Bayesian optimization stacking ensemble learning method. Taking the uplink and downlink catenary data of a high-speed railway line as an example, the preprocessed historical maintenance and maintenance data are input into the integrated prediction model of Bayesian hyperparameter optimization for training, and the root mean square error (RMSE) of the final optimized RUL prediction result is 0.068, with an R-square (R2) of 0.957, and a mean absolute error (MAE) of 0.053. The calculation example results show that the improved stacking ensemble algorithm improves the RMSE by 28.42%, 30.61% and 32.67% when compared with the extreme gradient boosting (XGBoost), support vector machine (SVM) and random forests (RF) algorithms, respectively. The improved accuracy prediction lays the foundation for targeted equipment maintenance and system maintenance performed before the catenary system fails, thus potentially saving both planned and reactive maintenance costs and time.
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Al-Refaie, Abbas, Banan Abu Hamdieh, and Natalija Lepkova. "Prediction of Maintenance Activities Using Generalized Sequential Pattern and Association Rules in Data Mining." Buildings 13, no. 4 (April 3, 2023): 946. http://dx.doi.org/10.3390/buildings13040946.
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This study proposed a data mining framework for predicting sequential patterns of maintenance activities. The framework consisted of data collection, prediction of maintenance activities with and without attributes, and then the comparison between prediction results. In data collection, historical data were collected regarding maintenance activities and product attributes. The generalized sequential pattern (GSP) and association rules were then applied to predict maintenance activities with and without attributes to determine the frequent sequential patterns and significant rules of maintenance activities. Finally, a comparison was performed between the sequences of maintenance activities with and without attributes. A real case study of washing machine products was presented to illustrate the developed framework. The results showed that the proposed framework effectively predicted the next maintenance activities and planning preventive maintenance based on product attributes. In conclusion, the data mining approach is found effective in determining the maintenance sequence that reduces downtime and thereby enhancing productivity and availability.
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TARIK, Mouna, Ayoub MNIAI, and Khalid JEBARI. "HYBRID FEATURE SELECTION AND SUPPORT VECTOR MACHINE FRAMEWORK FOR PREDICTING MAINTENANCE FAILURES." Applied Computer Science 19, no. 2 (June 30, 2023): 112–24. http://dx.doi.org/10.35784/acs-2023-18.
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The main aim of predictive maintenance is to minimize downtime, failure risks and maintenance costs in manufacturing systems. Over the past few years, machine learning methods gained ground with diverse and successful applications in the area of predictive maintenance. This study shows that performing preprocessing techniques such as oversampling and features selection for failure prediction, is promising. For instance, to handle imbalanced data, the SMOTE-Tomek method is used. For features selection, three different methods can be applied: Recursive Feature Elimination, Random Forest and Variance Threshold. The data considered in this paper for simulation is used in literature; it is applied to aircraft engine sensors measurements to predict engines failure, while the predicting algorithm used is a Support Vector Machine. The results show that classification accuracy can be significantly boosted by using the preprocessing techniques.
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Almazaideh, Mohammed, and Janos Levendovszky. "A predictive maintenance system for wireless sensor networks: a machine learning approach." Indonesian Journal of Electrical Engineering and Computer Science 25, no. 2 (February 1, 2022): 1047. http://dx.doi.org/10.11591/ijeecs.v25.i2.pp1047-1058.
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<p>Predictive maintenance system (PdM) is a new concept that helps system operators evaluate the current status of their systems, and it also assists in predicting the future quality of these systems and scheduling maintenance action. This paper proposes a PdM model that utilizes machine learning to predict the system’s operational status after M active steps based on L previous observations implemented by a feedforward neural network (FFNN). We use quantization and encoding schemes to reduce the complexity of the system. We apply the proposed model to build a PdM system for wireless sensors networks (WSNs), where our concern is to predict the state of the system as far as the quality of data transfer is concerned. The FFNN provides a forward prediction of the operational status of the network after M consecutive time steps in the future, based on the previous L readings of quality of service (QoS) requirements of WSN. We also demonstrate the relation between complexity and accuracy. We found that larger M leads to higher complexity and larger prediction error, where larger L entails higher complexity and smaller prediction error. We also investigate how quantization and encoding can reduce complexity to implement a real-time PdM system.</p>
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Cao, Qiushi, Ahmed Samet, Cecilia Zanni-Merk, François de Bertrand de Beuvron, and Christoph Reich. "Combining chronicle mining and semantics for predictive maintenance in manufacturing processes." Semantic Web 11, no. 6 (October 29, 2020): 927–48. http://dx.doi.org/10.3233/sw-200406.
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Within manufacturing processes, faults and failures may cause severe economic loss. With the vision of Industry 4.0, artificial intelligence techniques such as data mining play a crucial role in automatic fault and failure prediction. However, due to the heterogeneous nature of industrial data, data mining results normally lack both machine and human-understandable representation and interpretation of knowledge. This may cause the semantic gap issue, which stands for the incoherence between the knowledge extracted from industrial data and the interpretation of the knowledge from a user. To address this issue, ontology-based approaches have been used to bridge the semantic gap between data mining results and users. However, only a few existing ontology-based approaches provide satisfactory knowledge modeling and representation for all the essential concepts in predictive maintenance. Moreover, most of the existing research works merely focus on the classification of operating conditions of machines, while lacking the extraction of specific temporal information of failure occurrence. This brings obstacles for users to perform maintenance actions with the consideration of temporal constraints. To tackle these challenges, in this paper we introduce a novel hybrid approach to facilitate predictive maintenance tasks in manufacturing processes. The proposed approach is a combination of data mining and semantics, within which chronicle mining is used to predict the future failures of the monitored industrial machinery, and a Manufacturing Predictive Maintenance Ontology (MPMO) with its rule-based extension is used to predict temporal constraints of failures and to represent the predictive results formally. As a result, Semantic Web Rule Language (SWRL) rules are constructed for predicting the occurrence time of machinery failures in the future. The proposed rules provide explicit knowledge representation and semantic enrichment of failure prediction results, thus easing the understanding of the inferred knowledge. A case study on a semi-conductor manufacturing process is used to demonstrate our approach in detail. The evaluation of results shows that the MPMO ontology is free of bad practices in the structural, functional, and usability-profiling dimensions. The constructed SWRL rules posses more than 80% of True Positive Rate, Precision, and F-measure, which shows promising performance in failure prediction.
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Hosseini, Seyed Amirhossein, and Omar Smadi. "How Prediction Accuracy Can Affect the Decision-Making Process in Pavement Management System." Infrastructures 6, no. 2 (February 11, 2021): 28. http://dx.doi.org/10.3390/infrastructures6020028.
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One of the most important components of pavement management systems is predicting the deterioration of the network through performance models. The accuracy of the prediction model is important for prioritizing maintenance action. This paper describes how the accuracy of prediction models can have an effect on the decision-making process in terms of the cost of maintenance and rehabilitation activities. The process is simulating the propagation of the error between the actual and predicted values of pavement performance indicators. Different rate of error (10%, 30%, 50%, 70%, and 90%) was added into the result of prediction models. The results showed a strong correlation between the prediction models’ accuracy and the cost of maintenance and rehabilitation activities. The cost of treatment (in millions of dollars) over 20 years for five different scenarios increased from ($54.07–$92.95), ($53.89–$155.48), and ($74.41–$107.77) for asphalt, composite, and concrete pavement types, respectively. Increasing the rate of error also contributed to the prediction model, resulting in a higher benefit reduction rate.
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de Solminihac, Hernán, Marcelo G. Bustos, Aníbal L. Altamira, and Juan Pablo Covarrubias. "Functional distress modelling in Portland cement concrete pavements." Canadian Journal of Civil Engineering 30, no. 4 (August 1, 2003): 696–703. http://dx.doi.org/10.1139/l03-016.
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Concrete is widely used as a construction material in pavements by public and private agencies that administer highway networks because of its high durability and capacity to resist large traffic loads and very rigorous climates. Nevertheless, these agencies have to estimate the evolution of pavement performance to plan and optimize the application of adequate maintenance activities, allowing the pavement to be maintained at an optimum service level throughout its lifetime. Predictive distress models of the incremental type, that is, models capable of predicting annual increments of different distress indicators in the pavement, could be very useful tools in the implementation of maintenance plans, with minimal need for previous data, especially with regard to information on cumulative traffic loads. This paper offers incremental models for distress prediction in jointed plain concrete pavements, related to joint problems such as faulting and spalling, which clearly affect the pavement ride quality. The equations obtained allow for not only the calculation of distress predictions in analyzing road maintenance policies, but also the adjustment of the original designs of these pavements, to minimize the occurrence and magnitude of distress problems.Key words: concrete pavements, distress models, pavement performance, pavement management systems.
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Koprinkova-Hristova, P. "Reinforcement Learning for Predictive Maintenance of Industrial Plants." Information Technologies and Control 11, no. 1 (March 1, 2013): 21–28. http://dx.doi.org/10.2478/itc-2013-0004.
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Abstract The reinforcement learning is a well-known approach for solving optimization problems having limited information about plant dynamics. Its key element, named “critic” is aimed at prediction of future “punish/reward” signals received as a result of undertaken control actions. The main idea in the present work is to use such a “critic” element for prediction of approaching alarm situations based on limited measurement information from the industrial plant. In order to train the critic network in real time it is proposed to use a special kind of a fast trainable recurrent neural network, called Echo State Network (ESN). The approach proposed is demonstrated on an example for predictive maintenance of a mill fan in Maritsa East 2 Thermal Power Plant
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Custode, Leonardo Lucio, Hyunho Mo, Andrea Ferigo, and Giovanni Iacca. "Evolutionary Optimization of Spiking Neural P Systems for Remaining Useful Life Prediction." Algorithms 15, no. 3 (March 19, 2022): 98. http://dx.doi.org/10.3390/a15030098.
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Remaining useful life (RUL) prediction is a key enabler for predictive maintenance. In fact, the possibility of accurately and reliably predicting the RUL of a system, based on a record of its monitoring data, can allow users to schedule maintenance interventions before faults occur. In the recent literature, several data-driven methods for RUL prediction have been proposed. However, most of them are based on traditional (connectivist) neural networks, such as convolutional neural networks, and alternative mechanisms have barely been explored. Here, we tackle the RUL prediction problem for the first time by using a membrane computing paradigm, namely that of Spiking Neural P (in short, SN P) systems. First, we show how SN P systems can be adapted to handle the RUL prediction problem. Then, we propose the use of a neuro-evolutionary algorithm to optimize the structure and parameters of the SN P systems. Our results on two datasets, namely the CMAPSS and new CMAPSS benchmarks from NASA, are fairly comparable with those obtained by much more complex deep networks, showing a reasonable compromise between performance and number of trainable parameters, which in turn correlates with memory consumption and computing time.
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Alsolai, Hadeel, and Marc Roper. "The Impact of Ensemble Techniques on Software Maintenance Change Prediction: An Empirical Study." Applied Sciences 12, no. 10 (May 22, 2022): 5234. http://dx.doi.org/10.3390/app12105234.
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Various prediction models have been proposed by researchers to predict the change-proneness of classes based on source code metrics. However, some of these models suffer from low prediction accuracy because datasets exhibit high dimensionality or imbalanced classes. Recent studies suggest that using ensembles to integrate several models, select features, or perform sampling has the potential to resolve issues in the datasets and improve the prediction accuracy. This study aims to empirically evaluate the effectiveness of the ensemble models, feature selection, and sampling techniques on predicting change-proneness using different metrics. We conduct an empirical study to compare the performance of four machine learning models (naive Bayes, support vector machines, k-nearest neighbors, and random forests) on seven datasets for predicting change-proneness. We use two types of feature selection (relief and Pearson’s correlation coefficient) and three types of ensemble sampling techniques, which integrate different types of sampling techniques (SMOTE, spread sub-sample, and randomize). The results of this study reveal that the ensemble feature selection and sampling techniques yield improved prediction accuracy over most of the investigated models, and using sampling techniques increased the prediction accuracy of all models. Random forests provide a significant improvement over other prediction models and obtained the highest value of the average of the area under curve in all scenarios. The proposed ensemble feature selection and sampling techniques, along with the ensemble model (random forests), were found beneficial in improving the prediction accuracy of change-proneness.
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Velimirović, Lazar Z., Radmila Janković, Jelena D. Velimirović, and Aleksandar Janjić. "Wastewater Plant Reliability Prediction Using the Machine Learning Classification Algorithms." Symmetry 13, no. 8 (August 18, 2021): 1518. http://dx.doi.org/10.3390/sym13081518.
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One way to optimize wastewater treatment system infrastructure, its operations, monitoring, maintenance and management is through development of smart forecasting, monitoring and failure prediction systems using machine learning modeling. The aim of this paper was to develop a model that was able to predict a water pump failure based on the asymmetrical type of data obtained from sensors such as water levels, capacity, current and flow values. Several machine learning classification algorithms were used for predicting water pump failure. Using the classification algorithms, it was possible to make predictions of future values with a simple input of current values, as well as predicting probabilities of each sample belonging to each class. In order to build a prediction model, an asymmetrical type dataset containing the aforementioned variables was used.
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Bettig, Bernhard P., and Ray P. S. Han. "Predictive Maintenance Using the Rotordynamic Model of a Hydraulic Turbine-Generator Rotor." Journal of Vibration and Acoustics 120, no. 2 (April 1, 1998): 441–48. http://dx.doi.org/10.1115/1.2893849.
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A use of rotordynamic models in predictive maintenance is described in which variables characterizing the state of a deterioration mechanism are determined from online measurements. These variables are trended to determine the rate of deterioration and to perform a simulation to predict either the machine life or the maintenance period. Some useful terms for using models in predictive maintenance are defined and the prediction procedure is described. The procedure is demonstrated with a simple two degree-of-freedom example and the numerical model of an actual hydraulic turbine-generator rotor. Some benefits and problems associated with the implementation of the procedure are then discussed. It is considered that this procedure brings the possibility of a better understanding of deterioration processes and a resulting better life prediction.
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Abidi, Mustufa Haider, Muneer Khan Mohammed, and Hisham Alkhalefah. "Predictive Maintenance Planning for Industry 4.0 Using Machine Learning for Sustainable Manufacturing." Sustainability 14, no. 6 (March 14, 2022): 3387. http://dx.doi.org/10.3390/su14063387.
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With the advent of the fourth industrial revolution, the application of artificial intelligence in the manufacturing domain is becoming prevalent. Maintenance is one of the important activities in the manufacturing process, and it requires proper attention. To decrease maintenance costs and to attain sustainable operational management, Predictive Maintenance (PdM) has become important in industries. The principle of PdM is forecasting the next failure; thus, the respective maintenance is scheduled before the predicted failure occurs. In the construction of maintenance management, facility managers generally employ reactive or preventive maintenance mechanisms. However, reactive maintenance does not have the ability to prevent failure and preventive maintenance does not have the ability to predict the future condition of mechanical, electrical, or plumbing components. Therefore, to improve the facilities’ lifespans, such components are repaired in advance. In this paper, a PdM planning model is developed using intelligent methods. The developed method involves five main phases: (a) data cleaning, (b) data normalization, (c) optimal feature selection, (d) prediction network decision-making, and (e) prediction. Initially, the data pertaining to PdM are subjected to data cleaning and normalization in order to arrange the data within a particular limit. Optimal feature selection is performed next, to reduce redundant information. Optimal feature selection is performed using a hybrid of the Jaya algorithm and Sea Lion Optimization (SLnO). As the prediction values differ in range, it is difficult for machine learning or deep learning face to provide accurate results. Thus, a support vector machine (SVM) is used to make decisions regarding the prediction network. The SVM identifies the network in which prediction can be performed for the concerned range. Finally, the prediction is accomplished using a Recurrent Neural Network (RNN). In the RNN, the weight is optimized using the hybrid J-SLnO. A comparative analysis demonstrates that the proposed model can efficiently predict the future condition of components for maintenance planning by using two datasets—aircraft engine and lithium-ion battery datasets.
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Wang, Youdao, and Yifan Zhao. "Multi-Scale Remaining Useful Life Prediction Using Long Short-Term Memory." Sustainability 14, no. 23 (November 24, 2022): 15667. http://dx.doi.org/10.3390/su142315667.
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Predictive maintenance based on performance degradation is a crucial way to reduce maintenance costs and potential failures in modern complex engineering systems. Reliable remaining useful life (RUL) prediction is the main criterion for decision-making in predictive maintenance. Conventional model-based methods and data-driven approaches often fail to achieve an accurate prediction result using a single model for a complex system featuring multiple components and operational conditions, as the degradation pattern is usually nonlinear and time-varying. This paper proposes a novel multi-scale RUL prediction approach adopting the Long Short-Term Memory (LSTM) neural network. In the feature engineering phase, Pearson’s correlation coefficient is applied to extract the representative features, and an operation-based data normalisation approach is presented to deal with the cases where multiple degradation patterns are concealed in the sensor data. Then, a three-stage RUL target function is proposed, which segments the degradation process of the system into the non-degradation stage, the transition stage, and the linear degradation stage. The classification of these three stages is regarded as the small-scale RUL prediction, and it is achieved through processing sensor signals after the feature engineering using a novel LSTM-based binary classification algorithm combined with a correlation method. After that, a specific LSTM-based predictive model is built for the last two stages to produce a large-scale RUL prediction. The proposed approach is validated by comparing it with several state-of-the-art techniques based on the widely used C-MAPSS dataset. A significant improvement is achieved in RUL prediction performance in most subsets. For instance, a 40% reduction is achieved in Root Mean Square Error over the best existing method in subset FD001. Another contribution of the multi-scale RUL prediction approach is that it offers more degree of flexibility of prediction in the maintenance strategy depending on data availability and which degradation stage the system is in.
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Chui, Kwok Tai, Brij B. Gupta, and Pandian Vasant. "A Genetic Algorithm Optimized RNN-LSTM Model for Remaining Useful Life Prediction of Turbofan Engine." Electronics 10, no. 3 (January 25, 2021): 285. http://dx.doi.org/10.3390/electronics10030285.
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Understanding the remaining useful life (RUL) of equipment is crucial for optimal predictive maintenance (PdM). This addresses the issues of equipment downtime and unnecessary maintenance checks in run-to-failure maintenance and preventive maintenance. Both feature extraction and prediction algorithm have played crucial roles on the performance of RUL prediction models. A benchmark dataset, namely Turbofan Engine Degradation Simulation Dataset, was selected for performance analysis and evaluation. The proposal of the combination of complete ensemble empirical mode decomposition and wavelet packet transform for feature extraction could reduce the average root-mean-square error (RMSE) by 5.14–27.15% compared with six approaches. When it comes to the prediction algorithm, the results of the RUL prediction model could be that the equipment needs to be repaired or replaced within a shorter or a longer period of time. Incorporating this characteristic could enhance the performance of the RUL prediction model. In this paper, we have proposed the RUL prediction algorithm in combination with recurrent neural network (RNN) and long short-term memory (LSTM). The former takes the advantages of short-term prediction whereas the latter manages better in long-term prediction. The weights to combine RNN and LSTM were designed by non-dominated sorting genetic algorithm II (NSGA-II). It achieved average RMSE of 17.2. It improved the RMSE by 6.07–14.72% compared with baseline models, stand-alone RNN, and stand-alone LSTM. Compared with existing works, the RMSE improvement by proposed work is 12.95–39.32%.
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Kaya, Ertuğrul, Daniele Farioli, and Matteo Strano. "FEA Approach for Wear and Damage Prediction of Tools for the Progressive Die Stamping of Steel Washers." Key Engineering Materials 926 (July 22, 2022): 1168–77. http://dx.doi.org/10.4028/p-15186x.
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In progressive die stamping processes, maintenance activities caused by tool damage, and wear represent economic losses for companies. An effective predictive maintenance strategy can only be implemented if maintenance data coming from the operations are correlated to specific process-related information. As a part of a more general data-based predictive maintenance strategy, the main causes of tool damage and wear in a progressive die stamping factory that produces automotive metal washers have been identified by means of FEA simulations. In this study, the progressive die stamping of a dented conical washer is simulated with Transvalor FORGE FEA software by implementing the process parameters used in a real case. In this study, two indicators called FEAwear and FEAdamage are proposed for prediction of die wear and damage for tools with high risk of failure. For validating the accuracy of the FEA simulations, dimension and geometry comparisons are performed between FEA and real washer, and then real and FEA maximum press force comparison is performed. In the end, FEA simulations demonstrated their accuracy in predicting the stamping force of the press and the final part quality, and proposed FEA damage and wear indicators accurately predicted the most critical tools and stations, as confirmed by the real maintenance data. Finally, the simulations also correctly detected potential damage zones of the tools.
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Sresakoolchai, Jessada, and Sakdirat Kaewunruen. "Track Geometry Prediction Using Three-Dimensional Recurrent Neural Network-Based Models Cross-Functionally Co-Simulated with BIM." Sensors 23, no. 1 (December 30, 2022): 391. http://dx.doi.org/10.3390/s23010391.
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Railway track maintenance plays an important role in enabling safe, reliable, and seamless train operations and passenger comfort. Due to the increasing rail transportation, rolling stocks tend to run faster and the load tends to increase continuously. As a result, the track deteriorates quicker, and maintenance needs to be performed more frequently. However, more frequent maintenance activities do not guarantee a better overall performance of the railway system. It is crucial for rail infrastructure managers to optimize predictive and preventative maintenance. This study is the world’s first to develop deep machine learning models using three-dimensional recurrent neural network-based co-simulation models to predict track geometry parameters in the next year. Different recurrent neural network-based techniques are used to develop predictive models. In addition, a building information modeling (BIM) model is developed to integrate and cross-functionally co-simulate the track geometry measurement with the prediction for predictive and preventative maintenance purposes. From the study, the developed BIM models can be used to exchange information for predictive maintenance. Machine learning models provide the average R2 of 0.95 and the average mean absolute error of 0.56 mm. The insightful breakthrough demonstrates the potential of machine learning and BIM for predictive maintenance, which can promote the safety and cost effectiveness of railway maintenance.
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Marinelli, Marina, Sergios Lambropoulos, and Kleopatra Petroutsatou. "Earthmoving trucks condition level prediction using neural networks." Journal of Quality in Maintenance Engineering 20, no. 2 (May 6, 2014): 182–92. http://dx.doi.org/10.1108/jqme-09-2012-0031.
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Purpose – The purpose of this paper is to present an artificial neural network (ANN) model that predicts earthmoving trucks condition level using simple predictors; the model's performance is compared to the respective predictive accuracy of the statistical method of discriminant analysis (DA). Design/methodology/approach – An ANN-based predictive model is developed. The condition level predictors selected are the capacity, age, kilometers travelled and maintenance level. The relevant data set was provided by two Greek construction companies and includes the characteristics of 126 earthmoving trucks. Findings – Data processing identifies a particularly strong connection of kilometers travelled and maintenance level with the earthmoving trucks condition level. Moreover, the validation process reveals that the predictive efficiency of the proposed ANN model is very high. Similar findings emerge from the application of DA to the same data set using the same predictors. Originality/value – Earthmoving trucks’ sound condition level prediction reduces downtime and its adverse impact on earthmoving duration and cost, while also enhancing the maintenance and replacement policies effectiveness. This research proves that a sound condition level prediction for earthmoving trucks is achievable through the utilization of easy to collect data and provides a comparative evaluation of the results of two widely applied predictive methods.
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Feng, Jianshe, Haoshu Cai, Zongchang Liu, and Jay Lee. "A Systematic Framework for Maintenance Scheduling and Routing for Off-Shore Wind Farms by Minimizing Predictive Production Loss." E3S Web of Conferences 233 (2021): 01063. http://dx.doi.org/10.1051/e3sconf/202123301063.
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Maintenance scheduling and vessel routing are critical for the off-shore wind farm to reduce maintenance costs. In this research, a systematic framework that takes the advantage of predictive analysis for off-shore wind farm maintenance optimization is sketched and the optimization results are presented. The proposed framework consists of three different functional modules - the prognostic and diagnostic (P&D) module, the wind power prediction module, and the maintenance optimization module. The P&D module predicts and diagnoses the system failures based on the operational data of the wind turbine and generates the maintenance tasks for execution. The power prediction module predicts the weather conditions and the production of the wind turbine in the next 1-3 days, which will be helpful for maintenance task prioritization and scheduling. The optimization module absorbs information from the previous two modules as input and optimizes the overall maintenance costs. Comparing with the previous research works, this framework optimizes the maintenance cost in a more challenging situation by considering the predicted remaining useful life from the P&D module and also the future weather condition from the wind power prediction module. In the proposed framework, the maintenance scheduling and the vessel routing are optimized collaboratively with the consideration of real-time production loss. The result of the proposed framework is demonstrated on an off-shore wind farm and reduced maintenance cost is reported.
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Paprocka, Iwona, Wojciech M. Kempa, Krzysztof Kalinowski, and Cezary Grabowik. "A Production Scheduling Model with Maintenance." Advanced Materials Research 1036 (October 2014): 885–90. http://dx.doi.org/10.4028/www.scientific.net/amr.1036.885.
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In the paper, a production model with maintenance is presented. Successive failure-free times of a bottleneck are supposed to have predefined distributions and are followed by distributed times of repair. Having values of parameters: Mean Time To Failure and Mean Time of Repair, a predictive schedule is generated. To assess wastes due to unplanned events of the bottleneck, such as unplanned downtime the Overall Equipment Effectiveness indicator is applied. To assess failure rate of the bottleneck the Parts Per Million Opportunities indicator is applied. Prediction capability, detection capability of a failure and effects of the failure occurrence are evaluated and registered in the Exploitation Failure Mode and Effects Analysis form. The objective of the presented predictive scheduling model is to achieve: zero machines failures, zero defects, zero accidents at work.
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Caricato, A., A. Ficarella, and L. Spada Chiodo. "Prognostic techniques for aeroengine health assessment and Remaining Useful Life estimation." E3S Web of Conferences 312 (2021): 11017. http://dx.doi.org/10.1051/e3sconf/202131211017.
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Predictive maintenance is the latest frontier in the management and maintenance of many industrial assets, including aeroengines. Made possible by last decades advances in monitoring equipment and machine learning algorithms, it permits individual-based maintenance schedules, on the basis of performance monitoring and estimates resulting from the application of diagnostic and prognostic techniques, whether on ground or real time. Predictive maintenance results in operational cost reduction and asset usage optimization, if compared with traditional maintenance strategies, which instead may suffer from unanticipated failure or unnecessary maintenance and therefore higher operational costs. In the study, Remaining Useful Life (RUL) estimates will be carried out for different turbofan engines, based on historical individual and fleet data made available by the Prognostics Center of Excellence at NASA. The design of Prognostics and Health Management (PHM) algorithms requires at first an analysis of available data to identify which of them is effectively related to equipment degradation and hence could be useful in determining future system evolution and predicting failure. In particular, RUL prediction of test engines suffering from high pressure compressor fault with exponential degradation trend has been carried out with both regression and Artificial Neural Networks (ANNs). In turn, different regression models and neural network architectures have been compared, namely tree regression with different levels of tree depth, Gaussian Process Regression (GPR) with different kernel functions and Multilayer Perceptron (MLP) with one to three hidden layers and varying number of nodes. The objective is to demonstrate the capability of such machine learning algorithms to predict engine failure and thus their importance in supporting predictive maintenance planning, and to evaluate the quality of results in relation to the algorithm structure. Results show comparable performance in terms of Mean Absolute Error (MAE) and Root Mean Square Error (RMSE) of predicted with respect to actual RUL, in particular predictions obtained through recourse to multilayer perceptron reveal to be the most accurate, with a RMSE of 17.38 and a MAE of 12.50.
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Gopikuttan, Lasithan Lasyam, Shouri Puthan Veettil, and Rajesh Vazhayil Govindan. "Maintenance Initiation Prediction Incorporating Vibrations and System Availability." Advances in Technology Innovation 7, no. 3 (March 11, 2022): 181–94. http://dx.doi.org/10.46604/aiti.2022.8618.
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As per ISO-10816, electric motors up to 15 kW are classified as Class I machines, and the major reason for their failure is that the vibrations in them are above the alert limit. This study presents a new model for predicting the condition-based maintenance (CBM) initiation points through vibration measurement in a system of Class I machines. The proposed model follows the accelerated life testing (ALT) procedure. ALT includes the formation of an artificial wear environment in bearings to analyze the resultant system vibrations on system availability. The artificial wear environment created is close to the real industrial situation. The results show that the prediction of the CBM initiation points is based on the established relation between the system availability and vibrations. Furthermore, a relation between the available time for maintenance initiation and different vibration velocities is demonstrated.
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Milad, Abdalrhman Abrahim, Sayf A. Majeed, and Nur Izzi Md Yusoff. "Comparative Study of Utilising Neural Network and Response Surface Methodology for Flexible Pavement Maintenance Treatments." Civil Engineering Journal 6, no. 10 (October 1, 2020): 1895–905. http://dx.doi.org/10.28991/cej-2020-03091590.
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The use of Artificial Intelligence (AI) for the prediction of flexible pavement maintenance that is caused by distressing on the surface layer is crucial in the effort to increase the service life span of pavements as well as reduce government expenses. This study aimed to predict flexible pavement maintenance in tropical regions by using an Artificial Neural Network (ANN) and the Response Surface Methodology (RSM) for predicting models for pavement maintenance in the tropical region. However, to predict the performance of the treatment techniques for flexible pavements, we used critical criteria to choose our date from different sources to represent the situation of the current pavement. The effect of the distress condition on the flexible pavement surface performance was one of the criteria considered in our study. The data were chosen in this study for 288 sets of treatment techniques for flexible pavements. The input parameters used for the prediction were severity, density, road function, and Average Daily Traffic (ADT). The finding of regression models in (R2) values for the ANN prediction model is 0.93, while the (R2) values are (RSM) prediction model dependent on the full quadratic is 0.85. The results of two methods were compared for their predictive capabilities in terms of the coefficient of determination (𝑅2), the Mean Squared Error (MSE), and the Root Mean Square Error (RMSE), based on the dataset. The results showed that the prediction made utilizing ANN was very relevant to the goal in contrast to that made using the statistical program RSM based on different types of mathematical methods such as full quadratic, pure quadratic, interactions, and linear regression.
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Yeh, Chia-Hung, Min-Hui Lin, Chien-Hung Lin, Cheng-En Yu, and Mei-Juan Chen. "Machine Learning for Long Cycle Maintenance Prediction of Wind Turbine." Sensors 19, no. 7 (April 8, 2019): 1671. http://dx.doi.org/10.3390/s19071671.
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Within Internet of Things (IoT) sensors, the challenge is how to dig out the potentially valuable information from the collected data to support decision making. This paper proposes a method based on machine learning to predict long cycle maintenance time of wind turbines for efficient management in the power company. Long cycle maintenance time prediction makes the power company operate wind turbines as cost-effectively as possible to maximize the profit. Sensor data including operation data, maintenance time data, and event codes are collected from 31 wind turbines in two wind farms. Data aggregation is performed to filter out some errors and get significant information from the data. Then, the hybrid network is built to train the predictive model based on the convolutional neural network (CNN) and support vector machine (SVM). The experimental results show that the prediction of the proposed method reaches high accuracy, which helps drive up the efficiency of wind turbine maintenance.
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HORII, Masafumi, and Tadashi FUKUDA. "Pavement Ice Prediction System in Winter Maintenance." Doboku Gakkai Ronbunshu, no. 669 (2001): 243–51. http://dx.doi.org/10.2208/jscej.2001.669_243.
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