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Journal articles on the topic 'Remaining useful life estimation'

Author: Grafiati

Published: 4 June 2021

Last updated: 15 February 2022

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1

Ahmadzadeh, Farzaneh, and Jan Lundberg. "Remaining useful life estimation: review." International Journal of System Assurance Engineering and Management 5, no. 4 (September 26, 2013): 461–74. http://dx.doi.org/10.1007/s13198-013-0195-0.

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2

Johansson, Carl-Anders, Victor Simon, and Diego Galar. "Context Driven Remaining Useful Life Estimation." Procedia CIRP 22 (2014): 181–85. http://dx.doi.org/10.1016/j.procir.2014.07.129.

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3

Murali Krishna, K., and Dr K. Janardhan Reddy. "Remaining useful life estimation of a Product." Journal of Physics: Conference Series 1716 (December 2020): 012028. http://dx.doi.org/10.1088/1742-6596/1716/1/012028.

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4

Bechhoefer, Eric, and Marc Dube. "Contending Remaining Useful Life Algorithms." Annual Conference of the PHM Society 12, no. 1 (November 3, 2020): 9. http://dx.doi.org/10.36001/phmconf.2020.v12i1.1274.

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Operational readiness, reliability and safety are all enhanced through condition monitoring. That said, for many assets, there is still a need for a prognostic capability to calculate remaining useful life (RUL). RUL allows operation and maintenance personnel to better schedule assets, and logisticians to order long lead time part to help improve balance of plant/asset availability. While a number of RUL techniques have been reported, we have focused on fatigue crack growth models (as opposed to physics or deep learning of based models). This paper compares the performance of stress intensity models (linear elastic model, e.g. Paris’ Law), to Head’s theory (geomatical similarity hypothesis) and to Dislocation/Energy theories of crack growth. It will be shown that these models differ mainly in the crack growth exponent, and that this leads to large differences in the estimation of RUL during early state fault propagation, though the results of all three models converge as the RUL is shorted.

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5

Lyu, Jianhua, Rongrong Ying, Ningyun Lu, and Baili Zhang. "Remaining useful life estimation with multiple local similarities." Engineering Applications of Artificial Intelligence 95 (October 2020): 103849. http://dx.doi.org/10.1016/j.engappai.2020.103849.

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6

Nguyen, Thi-Bich-Lien, Mohand Djeziri, Bouchra Ananou, Mustapha Ouladsine, and Jacques Pinaton. "Remaining Useful Life estimation for noisy degradation trends." IFAC-PapersOnLine 48, no. 21 (2015): 85–90. http://dx.doi.org/10.1016/j.ifacol.2015.09.509.

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7

Jiang, Zengqiang, Dragan Banjevic, Mingcheng E., Andrew Jardine, and Qi Li. "Remaining useful life estimation of metropolitan train wheels considering measurement error." Journal of Quality in Maintenance Engineering 24, no. 4 (October 8, 2018): 422–36. http://dx.doi.org/10.1108/jqme-04-2016-0017.

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Purpose The purpose of this paper is to develop an approach for estimating the remaining useful life (RUL) of metropolitan train wheels considering measurement error. Design/methodology/approach The paper proposes a wear model of a metropolitan train wheel based on a discrete state space model; the model considers the wheel’s stochastic degradation and measurement error simultaneously. The paper estimates the RUL on the basis of the estimated degradation state. Finally, it presents a case study to verify the proposed approach. The results indicate that the proposed method is superior to methods that do not consider measurement error and can improve the accuracy of the estimated RUL. Findings RUL estimation is a key issue in condition-based maintenance and prognostics and health management. With the rapid development of advanced sensor technologies and data acquisition facilities for the maintenance of metropolitan train wheels, condition monitoring (CM) is becoming more accurate and more affordable, creating the possibility of estimating the RUL of wheels using CM data. However, the measurements of the wheels, especially the wayside measurements, are not yet precise enough. On the other hand, few existing studies of the RUL estimation of train wheels consider measurement error. Practical implications The approach described in this paper will make the RUL estimation of metropolitan train wheels easier and more precise. Originality/value Hundreds of million yuan are wasted every year due to over re-profiling of rail wheels in China. The ability to precisely estimate RUL will reduce the number of re-profiling activities and achieve significant economic benefits. More generally, the paper could enrich the body of knowledge of RUL estimation for a slowly degrading system considering measurement error.

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8

Malinowski, Simon, Brigitte Chebel-Morello, and Noureddine Zerhouni. "Remaining useful life estimation based on discriminating shapelet extraction." Reliability Engineering & System Safety 142 (October 2015): 279–88. http://dx.doi.org/10.1016/j.ress.2015.05.012.

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9

Wang, Hai-Kun, Yan-Feng Li, Yu Liu, Yuan-Jian Yang, and Hong-Zhong Huang. "Remaining useful life estimation under degradation and shock damage." Proceedings of the Institution of Mechanical Engineers, Part O: Journal of Risk and Reliability 229, no. 3 (March 10, 2015): 200–208. http://dx.doi.org/10.1177/1748006x15573046.

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10

Nguyen, Hoa Dinh. "A data-driven framework for remaining useful life estimation." Vietnam Journal of Science and Technology 55, no. 5 (October 20, 2017): 557. http://dx.doi.org/10.15625/2525-2518/55/5/8582.

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Remaining useful life (RUL) estimation is one of the most common tasks in the field of prognostics and structural health management. The aim of this research is to estimate the remaining useful life of an unspecified complex system using some data-driven approaches. The approaches are suitable for problems in which a data library of complete runs of a system is available. Given a non-complete run of the system, the RUL can be predicted using these approaches. Three main RUL prediction algorithms, which cover centralized data processing, decentralize data processing, and in-between, are introduced and evaluated using the data of PHM’08 Challenge Problem. The methods involve the use of some other data processing techniques including wavelets denoise and similarity search. Experiment results show that all of the approaches are effective in performing RUL prediction.

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11

Fan, Zhiliang, Guangbin Liu, Xiaosheng Si, Qi Zhang, and Qinghua Zhang. "Degradation data-driven approach for remaining useful life estimation." Journal of Systems Engineering and Electronics 24, no. 1 (February 2013): 173–82. http://dx.doi.org/10.1109/jsee.2013.00022.

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12

Drake, Joel, Robert Kratz, Matthew Smiley, John Dalessandro, and Mandyam Venkatesh. "Remaining useful life estimation of critical DIII-D subsystems." Fusion Engineering and Design 146 (September 2019): 491–95. http://dx.doi.org/10.1016/j.fusengdes.2018.12.100.

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13

Sanz-Gorrachategui, Ivan, Pablo Pastor-Flores, Milutin Pajovic, Ye Wang, Philip V. Orlik, Carlos Bernal-Ruiz, Antonio Bono-Nuez, and Jesus Sergio Artal-Sevil. "Remaining Useful Life Estimation for LFP Cells in Second-Life Applications." IEEE Transactions on Instrumentation and Measurement 70 (2021): 1–10. http://dx.doi.org/10.1109/tim.2021.3055791.

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14

Srinivasan, R., and T. Paul Robert. "Remaining Useful Life Prediction on Wind Turbine Gearbox." International Journal of Recent Technology and Engineering 9, no. 5 (January 30, 2021): 57–65. http://dx.doi.org/10.35940/ijrte.e5145.019521.

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This research proposes a methodology to estimate the reliability of gearbox using life data analysis and predict the Lifetime Use Estimation (LUE). Life data analysis involves collection of historical field replacements of gearbox and perform statistical analysis such as Weibull analysis to estimate the reliability. Remaining useful life is estimated by using Cumulative damage model and data-driven methods. The first approach is based on the physics of failure models of degradation and the second approach is based on the operational, environmental & loads data provided by the design team which is translated into a mathematical model that represent the behavior of the degradation. Data-driven method is used in this research, where the different performance data from components are exploited to model the degradation's behavior. LUE is used to make key business decisions such as planning of spares, service cost and increase availability of wind turbine. Gearbox is the heart of the wind turbine and it is made up of several stages of helical/planetary gears. Performance data is acquired separately for each of these stages and LUE is calculated individually. The individual LUE is then rolled up to estimate the overall Lifetime Use Estimation of gearbox. This will identify the weak link which is going to fail first and the failure mode which is driving the primary failure can be identified. Finally, corrective measures can be planned accordingly. The cumulated damage and LUE are estimated by using Inverse power law damage model along with Miner’s rule.

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15

Sikorska, J. Z., M. Hodkiewicz, and L. Ma. "Prognostic modelling options for remaining useful life estimation by industry." Mechanical Systems and Signal Processing 25, no. 5 (July 2011): 1803–36. http://dx.doi.org/10.1016/j.ymssp.2010.11.018.

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16

Ellsworth, Richard K. "Actuarial Methods, Survivor Curves, and Customer Remaining Useful Life Estimation." Business Valuation Review 30, no. 3 (September 2011): 104–10. http://dx.doi.org/10.5791/bvr-d-11-00007.1.

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17

Aydemir, Gurkan, and Burak Acar. "Anomaly monitoring improves remaining useful life estimation of industrial machinery." Journal of Manufacturing Systems 56 (July 2020): 463–69. http://dx.doi.org/10.1016/j.jmsy.2020.06.014.

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18

Pan, Donghui, Jia-Bao Liu, and Jinde Cao. "Remaining useful life estimation using an inverse Gaussian degradation model." Neurocomputing 185 (April 2016): 64–72. http://dx.doi.org/10.1016/j.neucom.2015.12.041.

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19

Singleton, Rodney K., Elias G. Strangas, and Selin Aviyente. "Extended Kalman Filtering for Remaining-Useful-Life Estimation of Bearings." IEEE Transactions on Industrial Electronics 62, no. 3 (March 2015): 1781–90. http://dx.doi.org/10.1109/tie.2014.2336616.

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20

Khelif, Racha, Brigitte Chebel-Morello, Simon Malinowski, Emna Laajili, Farhat Fnaiech, and Noureddine Zerhouni. "Direct Remaining Useful Life Estimation Based on Support Vector Regression." IEEE Transactions on Industrial Electronics 64, no. 3 (March 2017): 2276–85. http://dx.doi.org/10.1109/tie.2016.2623260.

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21

Chehade, Abdallah, Scott Bonk, and Kaibo Liu. "Sensory-Based Failure Threshold Estimation for Remaining Useful Life Prediction." IEEE Transactions on Reliability 66, no. 3 (September 2017): 939–49. http://dx.doi.org/10.1109/tr.2017.2695119.

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22

Chen, Chuang, Ningyun Lu, Bin Jiang, and Cunsong Wang. "A Risk-Averse Remaining Useful Life Estimation for Predictive Maintenance." IEEE/CAA Journal of Automatica Sinica 8, no. 2 (February 2021): 412–22. http://dx.doi.org/10.1109/jas.2021.1003835.

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23

Li, Shuaibing, Hui Ma, Tapan Kumar Saha, Yan Yang, and Guangning Wu. "On Particle Filtering for Power Transformer Remaining Useful Life Estimation." IEEE Transactions on Power Delivery 33, no. 6 (December 2018): 2643–53. http://dx.doi.org/10.1109/tpwrd.2018.2807386.

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24

Zhang, Bangcheng, Yuankun Sui, Qianying Bu, and Xiao He. "Remaining useful life estimation for micro switches of railway vehicles." Control Engineering Practice 84 (March 2019): 82–91. http://dx.doi.org/10.1016/j.conengprac.2018.10.010.

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25

Chen, Chuang, Ningyun Lu, Bin Jiang, and Cunsong Wang. "A Risk-Averse Remaining Useful Life Estimation for Predictive Maintenance." IEEE/CAA Journal of Automatica Sinica 8, no. 2 (February 2021): 412–22. http://dx.doi.org/10.1109/jas.2021.1003835.

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26

Behera, Sourajit, and Rajiv Misra. "Generative adversarial networks based remaining useful life estimation for IIoT." Computers & Electrical Engineering 92 (June 2021): 107195. http://dx.doi.org/10.1016/j.compeleceng.2021.107195.

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27

Hu, Yanyan, Shuai Qi, Xiaoling Xue, and Kaixiang Peng. "Remaining Useful Life Estimation Based on Asynchronous Multisource Monitoring Information Fusion." Journal of Control Science and Engineering 2017 (2017): 1–8. http://dx.doi.org/10.1155/2017/4139563.

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An asynchronous RUL fusion estimation algorithm is presented for the hidden degradation process with multiple asynchronous monitoring sensors based on multisource information fusion. Firstly, a state-space type model is established by modeling the stochastic degradation as a Wiener process and transforming asynchronous indirectly observations in the fusion period to the fusion time. The statistical characteristics of involved noises and their correlations are analyzed. Secondly, the estimate of the hidden degradation state is obtained by applying Kalman filtering with correlated noises to the established state-space model, where the synchronized observations are fused. Also, the unknown model parameters are recursively identified based on the Expectation-Maximization (EM) algorithm with the Generic Algorithm (GA) adopted to solve the maximization problem. Finally, the probability distribution of RUL is obtained using the fused degradation state estimation and the updated identification result of the model parameters. Simulation results show that the proposed fusion method has better performance than the RUL estimation with single sensor.

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28

Zhang, Bangcheng, Yubo Shao, Zhenchen Chang, Zhongbo Sun, and Yuankun Sui. "A Stochastic Deterioration Process Based Approach for Micro Switches Remaining Useful Life Estimation." Applied Sciences 9, no. 3 (February 12, 2019): 613. http://dx.doi.org/10.3390/app9030613.

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Real-time prediction of remaining useful life (RUL) is one of the most essential works inprognostics and health management (PHM) of the micro-switches. In this paper, a lineardegradation model based on an inverse Kalman filter to imitate the stochastic deterioration processis proposed. First, Bayesian posterior estimation and expectation maximization (EM) algorithm areused to estimate the stochastic parameters. Second, an inverse Kalman filter is delivered to solvethe errors in the initial parameters. In order to improve the accuracy of estimating nonlinear data,the strong tracking filtering (STF) method is used on the basis of Bayesian updating Third, theeffectiveness of the proposed approach is validated on an experimental data relating tomicro-switches for the rail vehicle. Additionally, it proposes another two methods for comparisonto illustrate the effectiveness of the method with an inverse Kalman filter in this paper. Inconclusion, a linear degradation model based on an inverse Kalman filter shall deal with errors inRUL estimation of the micro-switches excellently.

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29

AlDulaimi, Ali, Arash Mohammadi, and Amir Asif. "Noisy Multipath Parallel Hybrid Model for Remaining Useful Life Estimation (NMPM)." Annual Conference of the PHM Society 12, no. 1 (November 3, 2020): 10. http://dx.doi.org/10.36001/phmconf.2020.v12i1.1155.

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The parallel hybrid models of different deep neural networks architectures are the most promising approaches for remaining useful life (RUL) estimation. In light of that, this paper introduces for the first time in the literature a new parallel hybrid deep neural network (DNN) solution for RUL estimation, named as the Noisy Multipath Parallel Hybrid Model for Remaining Useful Life Estimation (NMPM). The proposed framework comprises of three parallel paths, the first one utilizes a noisy Bidirectional Long-short term memory (BLSTM) that used for extracting temporal features and learning the dependencies of sequence data in two directions, forward and backward, which can benefit completely from the input data. While the second parallel path employs noisy multilayer perceptron (MLP) that consists of three layers to extract different class of features. The third parallel path utilizes noisy convolutional neural networks (CNN) to extract another class of features. The concatenated output of the previous parallel paths is then fed into a noisy fusion center (NFC) to predict the RLU. The NMPM has been trained based on a noisy training to enhance the generalization behavior, as well as strengthen the model accuracy and robustness. The NMPM framework is tested and evaluated by using CMAPSS dataset provided by NASA.

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30

N., Bhalaji, and Rimi Chowdhury. "Remaining Useful Life (RUL) Estimation of Lead Acid Battery using Bayesian Approach." Journal of Electrical Engineering and Automation 2, no. 1 (March 15, 2020): 25–34. http://dx.doi.org/10.36548/jeea.2020.1.003.

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This article deals with Remaining Useful Life (RUL) estimation of Lead Acid Battery using a probabilistic approach which is Bayesian inference of Linear Regression. RUL estimation of lead acid battery plays a very crucial role as it can prevent the catastrophic failure for the system in which it is used to serve as a power supply mainly in automobiles. Although there are various methods for age estimation of lead acid battery, machine learning algorithms always played a major role in the same. In this paper we have implemented one such algorithm for the RUL estimation. Bayesian approach is a probabilistic method which can be used for predicting the RUL of the battery. Firstly, we present a framework for feature extraction and then the RUL estimation model is trained on Bayesian inference of Linear Regression. The proposed approach is then applied to the collected dataset from five differently aged batteries which have undergone some charging/discharging and load cycle test. The experiment result shows that the proposed approach can improve the accuracy of RUL estimation than the regular methods.

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31

TANG, Shengjin. "Step Stress Accelerated Degradation Process Modeling and Remaining Useful Life Estimation." Journal of Mechanical Engineering 50, no. 16 (2014): 33. http://dx.doi.org/10.3901/jme.2014.16.033.

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32

Park, Pangun, Mingyu Jung, and Piergiuseppe Di Marco. "Remaining Useful Life Estimation of Bearings Using Data-Driven Ridge Regression." Applied Sciences 10, no. 24 (December 16, 2020): 8977. http://dx.doi.org/10.3390/app10248977.

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Predicting the remaining useful life (RUL) of mechanical bearings is a challenging industrial task since RUL can differ even for the same equipment due to many uncertainties such as operating condition, model inaccuracy, and sensory noise in various industrial applications. This paper proposes the RUL prediction method combining analytical model-based and data-driven approaches to forecast when a failure will occur based on the time series data of bearings. Feature importance ranking and principal component analysis construct a reliable and predictable health indicator from various statistical time, frequency, and time–frequency domain features of the observed signal. The adaptive sliding window method then optimizes the parameters of the degradation model based on the ridge regression of the time series sequence with the sliding window. The proposed adaptive scheme provides significant performance improvement in terms of the RUL estimation accuracy and robustness against the possible errors of the degradation model compared to the traditional Bayesian approaches.

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33

Li, Xiang, Qian Ding, and Jian-Qiao Sun. "Remaining useful life estimation in prognostics using deep convolution neural networks." Reliability Engineering & System Safety 172 (April 2018): 1–11. http://dx.doi.org/10.1016/j.ress.2017.11.021.

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34

Ruiz-Tagle Palazuelos, Andrés, Enrique López Droguett, and Rodrigo Pascual. "A novel deep capsule neural network for remaining useful life estimation." Proceedings of the Institution of Mechanical Engineers, Part O: Journal of Risk and Reliability 234, no. 1 (August 7, 2019): 151–67. http://dx.doi.org/10.1177/1748006x19866546.

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With the availability of cheaper multi-sensor systems, one has access to massive and multi-dimensional sensor data for fault diagnostics and prognostics. However, from a time, engineering and computational perspective, it is often cost prohibitive to manually extract useful features and to label all the data. To address these challenges, deep learning techniques have been used in the recent years. Within these, convolutional neural networks have shown remarkable performance in fault diagnostics and prognostics. However, this model present limitations from a prognostics and health management perspective: to improve its feature extraction generalization capabilities and reduce computation time, ill-based pooling operations are employed, which require sub-sampling of the data, thus loosing potentially valuable information regarding an asset’s degradation process. Capsule neural networks have been recently proposed to address these problems with strong results in computer vision–related classification tasks. This has motivated us to extend capsule neural networks for fault prognostics and, in particular, remaining useful life estimation. The proposed model, architecture and algorithm are tested and compared to other state-of-the art deep learning models on the benchmark Commercial Modular Aero Propulsion System Simulation turbofans data set. The results indicate that the proposed capsule neural networks are a promising approach for remaining useful life prognostics from multi-dimensional sensor data.

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35

Haque, Moinul Shahidul, Seungdeog Choi, and Jeihoon Baek. "Auxiliary Particle Filtering-Based Estimation of Remaining Useful Life of IGBT." IEEE Transactions on Industrial Electronics 65, no. 3 (March 2018): 2693–703. http://dx.doi.org/10.1109/tie.2017.2740856.

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36

Si, Xiao-Sheng, Wenbin Wang, Chang-Hua Hu, Dong-Hua Zhou, and Michael G. Pecht. "Remaining Useful Life Estimation Based on a Nonlinear Diffusion Degradation Process." IEEE Transactions on Reliability 61, no. 1 (March 2012): 50–67. http://dx.doi.org/10.1109/tr.2011.2182221.

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37

Medjaher, Kamal, Diego Alejandro Tobon-Mejia, and Noureddine Zerhouni. "Remaining Useful Life Estimation of Critical Components With Application to Bearings." IEEE Transactions on Reliability 61, no. 2 (June 2012): 292–302. http://dx.doi.org/10.1109/tr.2012.2194175.

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38

Zhang, Xiaoyong, Pengcheng Xiao, Yingze Yang, Yijun Cheng, Bin Chen, Dianzhu Gao, Weirong Liu, and Zhiwu Huang. "Remaining Useful Life Estimation Using CNN-XGB With Extended Time Window." IEEE Access 7 (2019): 154386–97. http://dx.doi.org/10.1109/access.2019.2942991.

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39

Liu, Chunli, Qiang Li, and Kai Wang. "State-of-charge estimation and remaining useful life prediction of supercapacitors." Renewable and Sustainable Energy Reviews 150 (October 2021): 111408. http://dx.doi.org/10.1016/j.rser.2021.111408.

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40

Razavi-Far, Roozbeh, Shiladitya Chakrabarti, Mehrdad Saif, Enrico Zio, and Vasile Palade. "Extreme Learning Machine Based Prognostics of Battery Life." International Journal on Artificial Intelligence Tools 27, no. 08 (December 2018): 1850036. http://dx.doi.org/10.1142/s0218213018500367.

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This paper presents a prognostic scheme for estimating the remaining useful life of Lithium-ion batteries. The proposed scheme utilizes a prediction module that aims to obtain precise predictions for both short and long prediction horizons. The prediction module makes use of extreme learning machines for one-step and multi-step ahead predictions, using various prediction strategies, including iterative, direct and DirRec, which use the constant-current experimental capacity data for the estimation of the remaining useful life. The data-driven prognostic approach is highly dependent on the availability of high quantity of quality observations. Insufficient amount of available data can result in unsatisfactory prognostics. In this paper, the prognostics scheme is utilized to estimate the remaining useful life of a battery, with insufficient direct data available, but taking advantage of observations available from a fleet of similar batteries with similar working conditions. Experimental results show that the proposed prognostic scheme provides a fast and efficient estimation of the remaining useful life of the batteries and achieves superior results when compared with various state-of-the-art prediction techniques.

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41

Zhang, Jin, An Tong Gao, Rong Gang Chen, and Yu Sheng Han. "Discussion on the Li-Ion Battery Health Monitoring and Remaining-Useful-Life Prediction." Advanced Materials Research 724-725 (August 2013): 797–803. http://dx.doi.org/10.4028/www.scientific.net/amr.724-725.797.

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The Li-ion battery has high discharge voltage, long cycle life, good safety performance, no memory effect and other advantages. So it has being more and more used and concerned. This paper reviews various aspects of recent research and developments in Li-ion battery prognostics and health monitoring,and summarizes the techniques,algorithms and models used for state-of-charge estimation,voltage estimation,capacity estimation and remaining-useful-life prediction. Especially for state-of-charge estimation, this paper summed up many methods, such as current integration method, open circuit voltage method, Fuzzy logic, Autoregressive moving average model, Electrochemical impedance spectroscopy, Support vector machine and support vector machine based on Extended Kalman filter. And their advantages and disadvantages are summarized.

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42

Aria, Amin, Enrique Lopez Droguett, Shapour Azarm, and Mohammad Modarres. "Estimating damage size and remaining useful life in degraded structures using deep learning-based multi-source data fusion." Structural Health Monitoring 19, no. 5 (November 29, 2019): 1542–59. http://dx.doi.org/10.1177/1475921719890616.

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In this article, a new deep learning-based approach for online estimation of damage size and remaining useful life of structures is presented. The proposed approach consists of three modules. In the first module, a long short-term memory regression model is used to construct a sensor-based estimation of the damage size where different ranges of temporal correlations are considered for their effects on the accuracy of the damage size estimations. In the second module, a convolutional neural network semantic image segmentation approach is used to construct automated damage size estimations in which a pixel-wise classification is carried out on images of the damaged areas. Using physics-of-failure relations, frequency mismatches associated with sensor- and image-based size estimations are resolved. Finally, in the third module, damage size estimations obtained by the first two modules are fused together for an online remaining useful life estimation of the structure. Performance of the proposed approach is evaluated using sensor and image data obtained from a set of fatigue crack experiments performed on aluminum alloy 7075-T6 specimens. It is shown that using acoustic emission signals obtained from sensors and microscopic images in these experiments, the damage size estimations obtained from the proposed data fusion approach have higher accuracy than the sensor-based and higher frequency than the image-based estimations. Moreover, the accuracy of the data fusion estimations is found to be more than that of image-based estimations for the experiment with the largest sensor dataset. Based on the results obtained, it is concluded that the consideration of longer temporal correlations can lead to improvements in the accuracy of crack size estimations and, thus, a better remaining useful life estimation for structures.

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43

Tan, Wei Ming, and T. Hui Teo. "Remaining Useful Life Prediction Using Temporal Convolution with Attention." AI 2, no. 1 (February 14, 2021): 48–70. http://dx.doi.org/10.3390/ai2010005.

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Prognostic techniques attempt to predict the Remaining Useful Life (RUL) of a subsystem or a component. Such techniques often use sensor data which are periodically measured and recorded into a time series data set. Such multivariate data sets form complex and non-linear inter-dependencies through recorded time steps and between sensors. Many current existing algorithms for prognostic purposes starts to explore Deep Neural Network (DNN) and its effectiveness in the field. Although Deep Learning (DL) techniques outperform the traditional prognostic algorithms, the networks are generally complex to deploy or train. This paper proposes a Multi-variable Time Series (MTS) focused approach to prognostics that implements a lightweight Convolutional Neural Network (CNN) with attention mechanism. The convolution filters work to extract the abstract temporal patterns from the multiple time series, while the attention mechanisms review the information across the time axis and select the relevant information. The results suggest that the proposed method not only produces a superior accuracy of RUL estimation but it also trains many folds faster than the reported works. The superiority of deploying the network is also demonstrated on a lightweight hardware platform by not just being much compact, but also more efficient for the resource restricted environment.

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44

ZHANG, Xugang. "Remanufacturing Scheme Decision Model and Application Based on Remaining Useful Life Estimation." Journal of Mechanical Engineering 49, no. 07 (2013): 51. http://dx.doi.org/10.3901/jme.2013.07.051.

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45

Wen, Long, Yan Dong, and Liang Gao. "A new ensemble residual convolutional neural network for remaining useful life estimation." Mathematical Biosciences and Engineering 16, no. 2 (2019): 862–80. http://dx.doi.org/10.3934/mbe.2019040.

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Al-Dahidi, Sameer, Francesco Di Maio, Piero Baraldi, and Enrico Zio. "Remaining useful life estimation in heterogeneous fleets working under variable operating conditions." Reliability Engineering & System Safety 156 (December 2016): 109–24. http://dx.doi.org/10.1016/j.ress.2016.07.019.

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Baptista, Marcia, Elsa M P. Henriques, Ivo P de Medeiros, Joao P Malere, Cairo L Nascimento, and Helmut Prendinger. "Remaining useful life estimation in aeronautics: Combining data-driven and Kalman filtering." Reliability Engineering & System Safety 184 (April 2019): 228–39. http://dx.doi.org/10.1016/j.ress.2018.01.017.

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Wu, Yuting, Mei Yuan, Shaopeng Dong, Li Lin, and Yingqi Liu. "Remaining useful life estimation of engineered systems using vanilla LSTM neural networks." Neurocomputing 275 (January 2018): 167–79. http://dx.doi.org/10.1016/j.neucom.2017.05.063.

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Galar, D., U. Kumar, J. Lee, and W. Zhao. "Remaining Useful Life Estimation using Time Trajectory Tracking and Support Vector Machines." Journal of Physics: Conference Series 364 (May 28, 2012): 012063. http://dx.doi.org/10.1088/1742-6596/364/1/012063.

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Zio, Enrico, and Giovanni Peloni. "Particle filtering prognostic estimation of the remaining useful life of nonlinear components." Reliability Engineering & System Safety 96, no. 3 (March 2011): 403–9. http://dx.doi.org/10.1016/j.ress.2010.08.009.

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