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Journal articles on the topic 'Remainiing useful Life'

Author: Grafiati

Published: 4 June 2021

Last updated: 4 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

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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3

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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4

Vaidya, P., and M. Rausand. "Remaining useful life, technical health, and life extension." Proceedings of the Institution of Mechanical Engineers, Part O: Journal of Risk and Reliability 225, no. 2 (June 2011): 219–31. http://dx.doi.org/10.1177/1748007810394557.

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5

Pagitsch, Michael, Georg Jacobs, and Dennis Bosse. "Remaining Useful Life Determination for Wind Turbines." Journal of Physics: Conference Series 1452 (January 2020): 012052. http://dx.doi.org/10.1088/1742-6596/1452/1/012052.

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6

Banjevic, Dragan. "Remaining useful life in theory and practice." Metrika 69, no. 2-3 (December 4, 2008): 337–49. http://dx.doi.org/10.1007/s00184-008-0220-5.

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7

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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8

Agrawal, Shaashwat, Sagnik Sarkar, Gautam Srivastava, Praveen Kumar Reddy Maddikunta, and Thippa Reddy Gadekallu. "Genetically optimized prediction of remaining useful life." Sustainable Computing: Informatics and Systems 31 (September 2021): 100565. http://dx.doi.org/10.1016/j.suscom.2021.100565.

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9

Stevanović, Dragan, Aleksandar Janjić, and Dragan Tasić. "Methodology for circuit breakers remaining useful life assessment." Tehnika 74, no. 5 (2019): 687–93. http://dx.doi.org/10.5937/tehnika1905687s.

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10

Li, Min, Jiong Jiong Zhu, and Bin Long. "Particle Filter Approach for IGBT Remaining Useful Life." Advanced Materials Research 981 (July 2014): 86–89. http://dx.doi.org/10.4028/www.scientific.net/amr.981.86.

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With the increasingly widespread application, the requirement for PHM of IGBT is becoming gradually urgent. Based on particle filter theory, a method for remaining useful life (RUL) prediction of IGBT is proposed. Firstly, the deterioration parameters on-state VCE and ICE are extracted by temperature cycling test, then a model is developed based on the degradation trend exhibited by deterioration parameters. In the end, PF approach is applied to the IGBT's RUL prediction with the mentioned model. The results show that the proposed prediction method can achieve high prediction accuracy.

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11

Nadarajah, Saralees, and Samuel Kotz. "On the distribution of the remaining useful life." Mechanical Systems and Signal Processing 21, no. 1 (January 2007): 591. http://dx.doi.org/10.1016/j.ymssp.2006.02.004.

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12

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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13

Elsheikh, Ahmed, Soumaya Yacout, and Mohamed-Salah Ouali. "Bidirectional handshaking LSTM for remaining useful life prediction." Neurocomputing 323 (January 2019): 148–56. http://dx.doi.org/10.1016/j.neucom.2018.09.076.

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14

Helge Nystad, Bent, and Magnus Rasmussen. "Remaining useful life of natural gas export compressors." Journal of Quality in Maintenance Engineering 16, no. 2 (June 2010): 129–43. http://dx.doi.org/10.1108/13552511011048887.

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15

De Marco, Leonardo M., Jorge Otávio Trierweiler, and Marcelo Farenzena. "Determination of Remaining Useful Life in Cyclic Processes." Industrial & Engineering Chemistry Research 58, no. 48 (November 8, 2019): 22048–63. http://dx.doi.org/10.1021/acs.iecr.9b03182.

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16

Khorasgani, Hamed, Gautam Biswas, and Shankar Sankararaman. "Methodologies for system-level remaining useful life prediction." Reliability Engineering & System Safety 154 (October 2016): 8–18. http://dx.doi.org/10.1016/j.ress.2016.05.006.

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17

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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18

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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19

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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20

Li, Lin, Alfredo Alan Flores Saldivar, Yun Bai, and Yun Li. "Battery Remaining Useful Life Prediction with Inheritance Particle Filtering." Energies 12, no. 14 (July 19, 2019): 2784. http://dx.doi.org/10.3390/en12142784.

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Accurately forecasting a battery’s remaining useful life (RUL) plays an important role in the prognostics and health management of rechargeable batteries. An effective forecast is reported using a particle filter (PF), but it currently suffers from particle degeneracy and impoverishment deficiencies in RUL evaluations. In this paper, an inheritance PF is developed to predict lithium-ion battery RUL for the first time. A battery degradation model is first mapped onto a PF problem using the genetic algorithm (GA) framework. Then, a Lamarckian inheritance operator is designed to improve the light-weight particles by heavy-weight ones and thus to tackle particle degeneracy. In addition, the inheritance mechanism retains certain existing information to tackle particle impoverishment. The performance of the inheritance PF is compared with an elitism GA-based PF. The former has fewer tuning parameters than the latter and is less sensitive to tuning parameters. Both PFs are applied to the prediction of lithium-ion battery RUL, which is validated using capacity degradation data from the NASA Ames Research Center. The experimental results show that the inheritance PF method offers improved RUL prediction and wider applications. Further improvement is obtained with one-step ahead prediction when the charging and discharging cycles move along.

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21

Liu, Yingchao, Xiaofeng Hu, and Wenjuan Zhang. "Remaining useful life prediction based on health index similarity." Reliability Engineering & System Safety 185 (May 2019): 502–10. http://dx.doi.org/10.1016/j.ress.2019.02.002.

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22

Wang, Dong, Fangfang Yang, Yang Zhao, and Kwok-Leung Tsui. "Battery remaining useful life prediction at different discharge rates." Microelectronics Reliability 78 (November 2017): 212–19. http://dx.doi.org/10.1016/j.microrel.2017.09.009.

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23

Wang, Qihang, and Gang Wu. "Effective Latent Representation for Prediction of Remaining Useful Life." Computer Systems Science and Engineering 36, no. 1 (2021): 225–37. http://dx.doi.org/10.32604/csse.2021.014100.

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24

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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25

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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26

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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27

Li, Xiaopeng, Hong-Zhong Huang, Fuqiu Li, and Liming Ren. "Remaining useful life prediction model of the space station." Ekspolatacja i Niezawodnosc - Maintenance and Reliability 21, no. 3 (June 20, 2019): 501–10. http://dx.doi.org/10.17531/ein.2019.3.17.

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28

Changhao, Wan, Liu Zhiguo, Shengjin Tang, Xiaoyan Sun, and Xiaosheng Si. "Remaining Useful Life Prediction Under Imperfect Prior Degradation Information." IEEE Access 8 (2020): 189262–75. http://dx.doi.org/10.1109/access.2020.3030632.

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29

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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30

Meor Said, Mior Azman, Muhammad Hafizuddin Osman, Puteri Sri Melor Megat Yusoff, Shaharin Anwar Sulaiman, and Syed M. Afdhal Syed Ahmad Ghazali. "Determination of Remaining Useful Life of Gas Turbine Blade." MATEC Web of Conferences 38 (2016): 01011. http://dx.doi.org/10.1051/matecconf/20163801011.

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31

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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32

Li, Li, Zhen Zhao, Xiaoxiao Zhao, and Kuo-Yi Lin. "Gated Recurrent Unit Networks for Remaining Useful Life Prediction." IFAC-PapersOnLine 53, no. 2 (2020): 10498–504. http://dx.doi.org/10.1016/j.ifacol.2020.12.2795.

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33

Mo, Hyunho, Leonardo Lucio Custode, and Giovanni Iacca. "Evolutionary neural architecture search for remaining useful life prediction." Applied Soft Computing 108 (September 2021): 107474. http://dx.doi.org/10.1016/j.asoc.2021.107474.

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34

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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35

Noureddine, Rachid, and Asmaa Motrani. "Data-Driven Prognostic Framework for Remaining Useful Life Prediction." International Journal of Industrial and Systems Engineering 1, no. 1 (2021): 1. http://dx.doi.org/10.1504/ijise.2021.10039700.

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36

Wu, Ji-Yan, Min Wu, Zhenghua Chen, Xiao-Li Li, and Ruqiang Yan. "Degradation-Aware Remaining Useful Life Prediction With LSTM Autoencoder." IEEE Transactions on Instrumentation and Measurement 70 (2021): 1–10. http://dx.doi.org/10.1109/tim.2021.3055788.

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37

Okoh, C., R. Roy, J. Mehnen, and L. Redding. "Overview of Remaining Useful Life Prediction Techniques in Through-life Engineering Services." Procedia CIRP 16 (2014): 158–63. http://dx.doi.org/10.1016/j.procir.2014.02.006.

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38

Baheta, Aklilu Tesfamichael, Brilianto Brioann Boni Johanis, and Mohd Shahrizal Jasmani. "Prediction of Remaining Useful Life for Used Gas Turbine Blades." Advanced Materials Research 774-776 (September 2013): 370–74. http://dx.doi.org/10.4028/www.scientific.net/amr.774-776.370.

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Used turbine blades are replaced by new once based on manufacturers recommended useful life. However, depending on the turbine operating conditions the blades might have different creep service life. Thus, the aim of this study was to predict the remaining creep life and investigating the microstructure of used gas turbine blades. This was done by using Larson-Miller parameter and Robinson life fraction rules under certain conditions of stress and temperature. The change in microstructure of the materials was analyzed by using the Field Emission Scanning Electron Microscope (FESEM). The result shows that turbine blades suffered from several microstructure changes based on their service life. The method used to predict the remaining useful life of used turbines could be an input to a decision to repair or replace used turbine blades. Keywords: Larson-Miller parameter,life fraction rule, microstructure, creep life, turbine blades.

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39

Lu, Yi-Wei, Chia-Yu Hsu, and Kuang-Chieh Huang. "An Autoencoder Gated Recurrent Unit for Remaining Useful Life Prediction." Processes 8, no. 9 (September 15, 2020): 1155. http://dx.doi.org/10.3390/pr8091155.

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With the development of smart manufacturing, in order to detect abnormal conditions of the equipment, a large number of sensors have been used to record the variables associated with production equipment. This study focuses on the prediction of Remaining Useful Life (RUL). RUL prediction is part of predictive maintenance, which uses the development trend of the machine to predict when the machine will malfunction. High accuracy of RUL prediction not only reduces the consumption of manpower and materials, but also reduces the need for future maintenance. This study focuses on detecting faults as early as possible, before the machine needs to be replaced or repaired, to ensure the reliability of the system. It is difficult to extract meaningful features from sensor data directly. This study proposes a model based on an Autoencoder Gated Recurrent Unit (AE-GRU), in which the Autoencoder (AE) extracts the important features from the raw data and the Gated Recurrent Unit (GRU) selects the information from the sequences to forecast RUL. To evaluate the performance of the proposed AE-GRU model, an aircraft turbofan engine degradation simulation dataset provided by NASA was used and a comparison made of different recurrent neural networks. The results demonstrate that the AE-GRU is better than other recurrent neural networks, such as Long Short-Term Memory (LSTM) and GRU.

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40

Zhang, Yujie, Datong Liu, Jinxiang Yu, Yu Peng, and Xiyuan Peng. "EMA remaining useful life prediction with weighted bagging GPR algorithm." Microelectronics Reliability 75 (August 2017): 253–63. http://dx.doi.org/10.1016/j.microrel.2017.03.021.

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41

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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42

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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43

Yun, Yuri, Junyong Lee, Hwa-suk Oh, and Joo-Ho Choi. "Remaining useful life prediction of reaction wheel motor in satellites." JMST Advances 1, no. 3 (July 3, 2019): 219–26. http://dx.doi.org/10.1007/s42791-019-00020-5.

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44

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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45

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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46

Wang, Youdao, Yifan Zhao, and Sri Addepalli. "Remaining Useful Life Prediction using Deep Learning Approaches: A Review." Procedia Manufacturing 49 (2020): 81–88. http://dx.doi.org/10.1016/j.promfg.2020.06.015.

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47

Hou, Mengru, Dechang Pi, and Bingrong Li. "Similarity-based deep learning approach for remaining useful life prediction." Measurement 159 (July 2020): 107788. http://dx.doi.org/10.1016/j.measurement.2020.107788.

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48

She, Daoming, and Minping Jia. "A BiGRU method for remaining useful life prediction of machinery." Measurement 167 (January 2021): 108277. http://dx.doi.org/10.1016/j.measurement.2020.108277.

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49

Meng, Zong, Jing Li, Na Yin, and Zuozhou Pan. "Remaining useful life prediction of rolling bearing using fractal theory." Measurement 156 (May 2020): 107572. http://dx.doi.org/10.1016/j.measurement.2020.107572.

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50

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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