Collaborative Training of Data-Driven Remaining Useful Life Prediction Models Using Federated Learning
##plugins.themes.bootstrap3.article.main##
##plugins.themes.bootstrap3.article.sidebar##
Published
Oct 4, 2024
Wilhelm Söderkvist Vermelin
Madhav Mishra
Mattias P. Eng
Dag Andersson
Konstantinos Kyprianidis
Abstract
Remaining useful life prediction models are a central aspect of developing modern and capable prognostics and health management systems. Recently, such models are increasingly data-driven and based on various machine learning techniques, in particular deep neural networks. Such models are notoriously “data hungry”, i.e., to get adequate performance of such models, a substantial amount of diverse training data is needed. However, in several domains in which one would like to deploy data-driven remaining useful life models, there is a lack of data or data are distributed among several actors. Often these actors, for various reasons, cannot share data among themselves. In this paper a method for collaborative training of remaining useful life models based on federated learning is presented. In this setting, actors do not need to share locally held secret data, only model updates. Model updates are aggregated by a central server, and subsequently sent back to each of the clients, until convergence. There are numerous strategies for aggregating clients’ model updates and in this paper two strategies will be explored: 1) federated averaging and 2) federated learning with personalization layers. Federated averaging is the common baseline federated learning strategy where the clients’ models are averaged by the central server to update the global model. Federated averaging has been shown to have a limited ability to deal with non-identically and independently distributed data. To mitigate this problem, federated learning with personalization layers, a strategy similar to federated averaging but where each client is allowed to append custom layers to their local model, is explored. The two federated learning strategies will be evaluated on two datasets: 1) run-to-failure trajectories from power cycling of silicon-carbide metal-oxide semiconductor field-effect transistors, and 2) C-MAPSS, a well-known simulated dataset of turbofan jet engines. Two neural network model architectures commonly used in remaining useful life prediction, long short-term memory with multi-layer perceptron feature extractors, and convolutional gated recurrent unit, will be used for the evaluation. It is shown that similar or better performance is achieved when using federated learning compared to when the model is only trained on local data.
##plugins.themes.bootstrap3.article.details##
Keywords
remaining useful life, federated learning, machine learning, prognostics and health management, deep learning, electronics, turbofan jet engines
References
Abdelli, K., Cho, J. Y., & Pachnicke, S. (2021). Secure collaborative learning for predictive maintenance in optical networks [Conference paper]. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 13115 LNCS, 114 – 130. (Cited by: 0) doi: 10.1007/978-3-030-91625-1 7
Abdulrahman, S., Tout, H., Ould-Slimane, H., Mourad, A., Talhi, C., & Guizani, M. (2020, 10). A survey on federated learning: The journey from centralized to distributed on-site learning and beyond. IEEE Internet of Things Journal, PP. doi: 10.1109/JIOT.2020.3030072 Agarap, A. F. (2018). Deep learning using rectified linear units (relu). Retrieved from http://arxiv.org/abs/1803.08375 (cite arxiv:1803.08375 Comment: 7 pages, 11 figures, 9 tables)
Al-Dulaimi, A., Zabihi, S., Asif, A., & Mohammadi, A. (2019). A multimodal and hybrid deep neural network model for remaining useful life estimation [Article]. Computers in Industry, 108, 186 – 196. (Cited by: 167) doi: 10.1016/j.compind.2019.02.004
Arivazhagan, M. G., Aggarwal, V., Singh, A. K., & Choudhary, S. (2019). Federated learning with personalization layers. CoRR, abs/1912.00818. Retrieved from http://arxiv.org/abs/1912.00818
Arunan, A., Qin, Y., Li, X., & Yuen, C. (2023). A federated learning-based industrial health prognostics for heterogeneous edge devices using matched feature extraction [Article]. IEEE Transactions on Automation Science and Engineering, 1–15. (Cited by: 2; All Open Access, Green Open Access) doi: 10.1109/TASE.2023.3274648
Ballas, N., Yao, L., Pal, C., & Courville, A. C. (2016). Delving deeper into convolutional networks for learning video representations. In Y. Bengio & Y. LeCun (Eds.), 4th international conference on learning representations, ICLR 2016, san juan, puerto rico, may 2-4, 2016, conference track proceedings. Retrieved from http://arxiv.org/abs/1511.06432
Bemani, A., & Bj¨orsell, N. (2022). Aggregation strategy on federated machine learning algorithm for collaborative predictive maintenance [Article]. Sensors, 22(16). (Cited by: 11; All Open Access, Gold Open Access, Green Open Access) doi: 10.3390/s22166252
Ben Ali, J., Chebel-Morello, B., Saidi, L., Malinowski, S., & Fnaiech, F. (2015). Accurate bearing remaining useful life prediction based on weibull distribution and artificial neural network. Mechanical Systems and Signal Processing, 56-57, 150-172. Retrieved from https://www.sciencedirect.com/science/article/pii/S0888327014004087 doi: https://doi.org/10.1016/j.ymssp.2014.10.014
Beutel, D. J., Topal, T., Mathur, A., Qiu, X., Parcollet, T., & Lane, N. D. (2020). Flower: A friendly federated learning research framework. CoRR, abs/2007.14390. Retrieved from https://arxiv.org/abs/2007.14390
Celaya, J. R., Saxena, A., Saha, S., & Goebel, K. F. (2011). Prognostics of power mosfets under thermal stress accelerated aging using data-driven and model-based methodologies. In (Vol. 3, p. 1995). doi: 10.36001/phmconf.2011.v3i1.1995
Chaoub, A., Voisin, A., Cerisara, C., & Iung, B. (2021). Learning representations with end-to-end models for improved remaining useful life prognostics. CoRR, abs/2104.05049. Retrieved from https://arxiv.org/abs/2104.05049
Che, C., Wang, H., Fu, Q., & Ni, X. (2019). Combining multiple deep learning algorithms for prognostic and health management of aircraft [Review]. Aerospace Science and Technology, 94. (Cited by: 87) doi: 10.1016/j.ast.2019.105423
Chen, Q., Nicholson, G., Ye, J., Zhao, Y., & Roberts, C. (2020). Estimating residual life distributions of complex operational systems using a remaining maintenance free operating period (rmfop)-based methodology. Sensors, 20(19). Retrieved from https://www.mdpi.com/1424-8220/20/19/5504 doi: 10.3390/s20195504
Chen, X., Wang, H., Lu, S., Xu, J., & Yan, R. (2023). Remaining useful life prediction of turbofan engine using global health degradation representation in federated learning. Reliability Engineering & System Safety, 239, 109511. Retrieved from https://www.sciencedirect.com/science/article/pii/S0951832023004258 doi: https://doi.org/10.1016/j.ress.2023.109511
Clevert, D.-A., Unterthiner, T., & Hochreiter, S. (2016). Fast and accurate deep network learning by exponential linear units (elus). In Y. Bengio & Y. LeCun (Eds.), Iclr (poster). Retrieved from http://dblp.uni-trier.de/db/conf/iclr/iclr2016.html#ClevertUH15
Demus, J., Sysoeva, V., Cheng, Q., Boubin, M., Siraj, A., & Scott, M. (2019). Mosfet junction temperature measurements using conducted electromagnetic emissions and support vector machines [Conference paper]. In (p. 2973 – 2978). Institute of Electrical and Electronics Engineers Inc. (Cited by: 0) doi: 10.1109/ECCE.2019.8912938
Dhada, M. H., Parlikad, A. K., & Palau, A. S. (2020). Federated learning for collaborative prognosis. Retrieved from https://api.semanticscholar.org/CorpusID:235049866
Doulamis, A. D., Hou, G., Xu, S., Zhou, N., Yang, L., & Fu, Q. (2020). Remaining useful life estimation using deep convolutional generative adversarial networks based on an autoencoder scheme. Computational Intelligence and Neuroscience, 2020, 9601389. Retrieved from https://doi.org/10.1155/2020/9601389 doi: 10.1155/2020/9601389
Du, N. H., Long, N. H., Ha, K. N., Hoang, N. V., Huong, T. T., & Tran, K. P. (2023). Trans-lighter: A light-weight federated learning-based architecture for remaining useful lifetime prediction. Computers in Industry, 148, 103888. Retrieved from https://www.sciencedirect.com/science/article/pii/S0166361523000386 doi: https://doi.org/10.1016/j.compind.2023.103888
Falcon, W., & team, T. P. L. (2019, 3). Pytorch lightning. Retrieved from https://www.pytorchlightning.ai doi: 10.5281/zenodo.3828935
Goodfellow, I., Bengio, Y., & Courville, A. (2016). Deep learning. MIT Press. (http://www.deeplearningbook.org)
Guo, L., Yu, Y., Qian, M., Zhang, R., Gao, H., & Cheng, Z. (2023). Fedrul: A new federated learning method for edge-cloud collaboration based remaining useful life prediction of machines. IEEE/ASME Transactions on Mechatronics, 28(1), 350-359. doi: 10.1109/TMECH.2022.3195524
Haris, M., Hasan, M. N., Jahanzeb Hussain Pirzada, S., & Qin, S. (2020). Bayesian optimized long-short term memory recurrent neural network for prognostics of thermally aged power mosfets [Conference paper]. In S. M., M. M.A., & N. S. (Eds.), . Institute of Electrical and Electronics Engineers Inc. (Cited by: 2) doi: 10.1109/RAEECS50817.2020.9265738
Hestness, J., Narang, S., Ardalani, N., Diamos, G. F., Jun, H., Kianinejad, H., . . . Zhou, Y. (2017). Deep learning scaling is predictable, empirically. CoRR, abs/1712.00409. Retrieved from http://arxiv.org/abs/1712.00409
Hochreiter, S., & Schmidhuber, J. (1997). Long short-term memory. Neural computation, 9(8), 1735–1780.
Huang, C.-G., Zhu, J., Han, Y., & Peng, W. (2022). A novel bayesian deep dual network with unsupervised domain adaptation for transfer fault prognosis across different machines. IEEE Sensors Journal, 22(8), 7855-7867. doi: 10.1109/JSEN.2021.3133622
Kamei, S., & Taghipour, S. (2023). A comparison study of centralized and decentralized federated learning approaches utilizing the transformer architecture for estimating remaining useful life [Article]. Reliability Engineering and System Safety, 233. (Cited by: 6) doi: 10.1016/j.ress.2023.109130
Kingma, D. P., & Ba, J. (2015). Adam: A method for stochastic optimization. In Y. Bengio & Y. LeCun (Eds.), 3rd international conference on learning representations, ICLR 2015, san diego, ca, usa, may 7-9, 2015, conference track proceedings. Retrieved from http://arxiv.org/abs/1412.6980
Lallart, M., Wu, B., Li, W., & Qiu, M.-q. (2017). Remaining useful life prediction of bearing with vibration signals based on a novel indicator. Shock and Vibration, 2017, 8927937. Retrieved from https://doi.org/10.1155/2017/8927937 doi: 10.1155/2017/8927937
LeCun, Y., Bengio, Y., & Hinton, G. (2015). Deep learning. Nature, 521(7553), 436–444. Retrieved from https://doi.org/10.1038/nature14539 doi: 10.1038/nature14539
Li, Q., Wen, Z., Wu, Z., Hu, S., Wang, N., Li, Y., . . . He, B. (2023, apr). A survey on federated learning systems: Vision, hype and reality for data privacy and protection. IEEE Transactions on Knowledge; Data Engineering, 35(04), 3347-3366. doi: 10.1109/TKDE.2021.3124599
Li, T. (2024). Particle filter-based fatigue damage prognosis using prognostic-aided model updating. Mechanical Systems and Signal Processing, 211, 111244. Retrieved from https://www.sciencedirect.com/science/article/pii/S0888327024001420 doi: https://doi.org/10.1016/j.ymssp.2024.111244
Liaw, R., Liang, E., Nishihara, R., Moritz, P., Gonzalez, J. E., & Stoica, I. (2018). Tune: A research platform for distributed model selection and training. arXiv preprint arXiv:1807.05118.
Loshchilov, I., & Hutter, F. (2019). Decoupled weight decay regularization. In International conference on learning representations. Retrieved from https://openreview.net/forum?id=Bkg6RiCqY7
Mahamad, A. K., Saon, S., & Hiyama, T. (2010). Predicting remaining useful life of rotating machinery based artificial neural network. Computers & Mathematics with Applications, 60(4), 1078-1087. Retrieved from https://www.sciencedirect.com/science/article/pii/S0898122110002555 (PCO’ 2010) doi: https://doi.org/10.1016/j.camwa.2010.03.065
McMahan, B., Moore, E., Ramage, D., Hampson, S., & Arcas, B. A. y. (2017, 20–22 Apr). Communication-efficient learning of deep networks from decentralized data. In A. Singh & J. Zhu (Eds.), Proceedings of the 20th international conference on artificial intelligence and statistics (Vol. 54, pp. 1273–1282). PMLR. Retrieved from https://proceedings.mlr.press/v54/mcmahan17a.html
Meriem, H., Nora, H., & Samir, O. (2023). Predictive maintenance for smart industrial systems: A roadmap. Procedia Computer Science, 220, 645-650. Retrieved from https://www.sciencedirect.com/science/article/pii/S1877050923006178 (The 14th International Conference on Ambient Systems, Networks and Technologies Networks (ANT) and The 6th International Conference on Emerging Data and Industry 4.0 (EDI40)) doi: https://doi.org/10.1016/j.procs.2023.03.082
Moshawrab, M., Adda, M., Bouzouane, A., Ibrahim, H., & Raad, A. (2023). Reviewing federated learning aggregation algorithms; strategies, contributions, limitations and future perspectives. Electronics, 12(10). Retrieved from https://www.mdpi.com/2079-9292/12/10/2287 doi: 10.3390/electronics12102287
Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., . . . Chintala, S. (2019). Pytorch: An imperative style, high-performance deep learning library. In Advances in neural information processing systems 32 (pp. 8024–8035). Curran Associates, Inc. Retrieved from http://papers.neurips.cc/paper/9015-pytorch-an-imperative-style-high-performance-deep-learning-library.pdf
Pecht, M., & Kang, M. (2018). Prognostics and health management of electronics : fundamentals, machine learning, and internet of things (Second edition. ed.). Hoboken, New Jersey: John Wiley & Sons.
Ren, H., Du, X., Yu, Y., Wang, J., Zhou, J., & Peng, Y. (2022). Power mosfet lifetime prediction method based on optimized long short-term memory neural network [Conference paper]. Institute of Electrical and Electronics Engineers Inc. (Cited by: 0) doi: 10.1109/ECCE50734.2022.9947640
Sahu, A. K., Li, T., Sanjabi, M., Zaheer, M., Talwalkar, A., & Smith, V. (2018). On the convergence of federated optimization in heterogeneous networks. CoRR, abs/1812.06127. Retrieved from http://arxiv.org/abs/1812.06127
Saxena, A., Goebel, K., Simon, D., & Eklund, N. (2008). Damage propagation modeling for aircraft engine run-to-failure simulation. In 2008 international conference on prognostics and health management (p. 1-9). doi: 10.1109/PHM.2008.4711414
Singh, J., Darpe, A. K., & Singh, S. P. (2020, may). Bearing remaining useful life estimation using an adaptive data-driven model based on health state change point identification and k-means clustering. Measurement Science and Technology, 31(8), 085601. Retrieved from https://dx.doi.org/10.1088/1361-6501/ab6671 doi: 10.1088/1361-6501/ab6671
Söderkvist Vermelin, W., L¨ovberg, A., Misiorny, M., P. Eng, M., & Brinkfeldt, K. (2023). Data-driven remaining useful life estimation of discrete power electronic devices. In Proceedings of the 33rd european safety and reliability conference (esrel 2023) (Vol. 33). European Safety and Reliability Conference. Retrieved from https://urn.kb.se/resolve?urn=urn:nbn:se:ri:diva-67109 doi: 10.3850/978-981-18-8071-1-4procd
Tian, Z. (2009). An artificial neural network approach for remaining useful life prediction of equipments subject to condition monitoring. In 2009 8th international conference on reliability, maintainability and safety (p. 143-148). doi: 10.1109/ICRMS.2009.5270220
Tian, Z., Wong, L., & Safaei, N. (2010). A neural network approach for remaining useful life prediction utilizing both failure and suspension histories. Mechanical Systems and Signal Processing, 24(5), 1542-1555. Retrieved from https://www.sciencedirect.com/science/article/pii/S088832700900377X (Special Issue: Operational Modal Analysis) doi: https://doi.org/10.1016/j.ymssp.2009.11.005
Van Rossum, G., & Drake, F. L. (2009). Python 3 reference manual. Scotts Valley, CA: CreateSpace.
Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A. N., . . . Polosukhin, I. (2017). Attention is all you need. In I. Guyon et al. (Eds.), Advances in neural information processing systems (Vol. 30). Curran Associates, Inc. Retrieved from https://proceedings.neurips.cc/paper files/paper/2017/file/3f5ee243547dee91fbd053c1c4a845aa-Paper.pdf
Vibhorpandhare, Jia, X., & Lee, J. (2021). Collaborative prognostics for machine fleets using a novel federated baseline learner.. Retrieved from https://api.semanticscholar.org/CorpusID:244645140
Wang, J., Wen, G., Yang, S., & Liu, Y. (2019). Remaining useful life estimation in prognostics using deep bidirectional lstm neural network [Conference paper]. In D. P., L. C., Y. S., D. P., & S. R.-V. (Eds.), (p. 1037 – 1042). Institute of Electrical and Electronics Engineers Inc. (Cited by: 121) doi: 10.1109/PHM-Chongqing.2018.00184
Wu, S.-j., Gebraeel, N., Lawley, M. A., & Yih, Y. (2007). A neural network integrated decision support system for condition-based optimal predictive maintenance policy. IEEE Transactions on Systems, Man, and Cybernetics - Part A: Systems and Humans, 37(2), 226-236. doi: 10.1109/TSMCA.2006.886368
Xia, M., Li, T., Shu, T., Wan, J., de Silva, C. W., & Wang, Z. (2019). A two-stage approach for the remaining useful life prediction of bearings using deep neural networks. IEEE Transactions on Industrial Informatics, 15(6), 3703-3711. doi: 10.1109/TII.2018.2868687
Xu, J., Duan, S., Chen, W., Wang, D., & Fan, Y. (2022). Sacgnet: A remaining useful life prediction of bearing with self-attention augmented convolution gru network. Lubricants, 10(2). Retrieved from https://www.mdpi.com/2075-4442/10/2/21 doi: 10.3390/lubricants10020021
Zhang, J.,Wang, P., Yan, R., & Gao, R. X. (2018). Long short-term memory for machine remaining life prediction [Article]. Journal of Manufacturing Systems, 48, 78 – 86. (Cited by: 302) doi: 10.1016/j.jmsy.2018.05.011
Zheng, S., Ristovski, K., Farahat, A., & Gupta, C. (2017). Long short-term memory network for remaining useful life estimation. In 2017 ieee international conference on prognostics and health management (icphm) (p. 88-95). doi: 10.1109/ICPHM.2017.7998311
Zhu, J., Chen, N., & Shen, C. (2020). A new data-driven transferable remaining useful life prediction approach for bearing under different working conditions. Mechanical Systems and Signal Processing, 139, 106602. Retrieved from https://www.sciencedirect.com/science/article/pii/S0888327019308234 doi: https://doi.org/10.1016/j.ymssp.2019.106602
Abdulrahman, S., Tout, H., Ould-Slimane, H., Mourad, A., Talhi, C., & Guizani, M. (2020, 10). A survey on federated learning: The journey from centralized to distributed on-site learning and beyond. IEEE Internet of Things Journal, PP. doi: 10.1109/JIOT.2020.3030072 Agarap, A. F. (2018). Deep learning using rectified linear units (relu). Retrieved from http://arxiv.org/abs/1803.08375 (cite arxiv:1803.08375 Comment: 7 pages, 11 figures, 9 tables)
Al-Dulaimi, A., Zabihi, S., Asif, A., & Mohammadi, A. (2019). A multimodal and hybrid deep neural network model for remaining useful life estimation [Article]. Computers in Industry, 108, 186 – 196. (Cited by: 167) doi: 10.1016/j.compind.2019.02.004
Arivazhagan, M. G., Aggarwal, V., Singh, A. K., & Choudhary, S. (2019). Federated learning with personalization layers. CoRR, abs/1912.00818. Retrieved from http://arxiv.org/abs/1912.00818
Arunan, A., Qin, Y., Li, X., & Yuen, C. (2023). A federated learning-based industrial health prognostics for heterogeneous edge devices using matched feature extraction [Article]. IEEE Transactions on Automation Science and Engineering, 1–15. (Cited by: 2; All Open Access, Green Open Access) doi: 10.1109/TASE.2023.3274648
Ballas, N., Yao, L., Pal, C., & Courville, A. C. (2016). Delving deeper into convolutional networks for learning video representations. In Y. Bengio & Y. LeCun (Eds.), 4th international conference on learning representations, ICLR 2016, san juan, puerto rico, may 2-4, 2016, conference track proceedings. Retrieved from http://arxiv.org/abs/1511.06432
Bemani, A., & Bj¨orsell, N. (2022). Aggregation strategy on federated machine learning algorithm for collaborative predictive maintenance [Article]. Sensors, 22(16). (Cited by: 11; All Open Access, Gold Open Access, Green Open Access) doi: 10.3390/s22166252
Ben Ali, J., Chebel-Morello, B., Saidi, L., Malinowski, S., & Fnaiech, F. (2015). Accurate bearing remaining useful life prediction based on weibull distribution and artificial neural network. Mechanical Systems and Signal Processing, 56-57, 150-172. Retrieved from https://www.sciencedirect.com/science/article/pii/S0888327014004087 doi: https://doi.org/10.1016/j.ymssp.2014.10.014
Beutel, D. J., Topal, T., Mathur, A., Qiu, X., Parcollet, T., & Lane, N. D. (2020). Flower: A friendly federated learning research framework. CoRR, abs/2007.14390. Retrieved from https://arxiv.org/abs/2007.14390
Celaya, J. R., Saxena, A., Saha, S., & Goebel, K. F. (2011). Prognostics of power mosfets under thermal stress accelerated aging using data-driven and model-based methodologies. In (Vol. 3, p. 1995). doi: 10.36001/phmconf.2011.v3i1.1995
Chaoub, A., Voisin, A., Cerisara, C., & Iung, B. (2021). Learning representations with end-to-end models for improved remaining useful life prognostics. CoRR, abs/2104.05049. Retrieved from https://arxiv.org/abs/2104.05049
Che, C., Wang, H., Fu, Q., & Ni, X. (2019). Combining multiple deep learning algorithms for prognostic and health management of aircraft [Review]. Aerospace Science and Technology, 94. (Cited by: 87) doi: 10.1016/j.ast.2019.105423
Chen, Q., Nicholson, G., Ye, J., Zhao, Y., & Roberts, C. (2020). Estimating residual life distributions of complex operational systems using a remaining maintenance free operating period (rmfop)-based methodology. Sensors, 20(19). Retrieved from https://www.mdpi.com/1424-8220/20/19/5504 doi: 10.3390/s20195504
Chen, X., Wang, H., Lu, S., Xu, J., & Yan, R. (2023). Remaining useful life prediction of turbofan engine using global health degradation representation in federated learning. Reliability Engineering & System Safety, 239, 109511. Retrieved from https://www.sciencedirect.com/science/article/pii/S0951832023004258 doi: https://doi.org/10.1016/j.ress.2023.109511
Clevert, D.-A., Unterthiner, T., & Hochreiter, S. (2016). Fast and accurate deep network learning by exponential linear units (elus). In Y. Bengio & Y. LeCun (Eds.), Iclr (poster). Retrieved from http://dblp.uni-trier.de/db/conf/iclr/iclr2016.html#ClevertUH15
Demus, J., Sysoeva, V., Cheng, Q., Boubin, M., Siraj, A., & Scott, M. (2019). Mosfet junction temperature measurements using conducted electromagnetic emissions and support vector machines [Conference paper]. In (p. 2973 – 2978). Institute of Electrical and Electronics Engineers Inc. (Cited by: 0) doi: 10.1109/ECCE.2019.8912938
Dhada, M. H., Parlikad, A. K., & Palau, A. S. (2020). Federated learning for collaborative prognosis. Retrieved from https://api.semanticscholar.org/CorpusID:235049866
Doulamis, A. D., Hou, G., Xu, S., Zhou, N., Yang, L., & Fu, Q. (2020). Remaining useful life estimation using deep convolutional generative adversarial networks based on an autoencoder scheme. Computational Intelligence and Neuroscience, 2020, 9601389. Retrieved from https://doi.org/10.1155/2020/9601389 doi: 10.1155/2020/9601389
Du, N. H., Long, N. H., Ha, K. N., Hoang, N. V., Huong, T. T., & Tran, K. P. (2023). Trans-lighter: A light-weight federated learning-based architecture for remaining useful lifetime prediction. Computers in Industry, 148, 103888. Retrieved from https://www.sciencedirect.com/science/article/pii/S0166361523000386 doi: https://doi.org/10.1016/j.compind.2023.103888
Falcon, W., & team, T. P. L. (2019, 3). Pytorch lightning. Retrieved from https://www.pytorchlightning.ai doi: 10.5281/zenodo.3828935
Goodfellow, I., Bengio, Y., & Courville, A. (2016). Deep learning. MIT Press. (http://www.deeplearningbook.org)
Guo, L., Yu, Y., Qian, M., Zhang, R., Gao, H., & Cheng, Z. (2023). Fedrul: A new federated learning method for edge-cloud collaboration based remaining useful life prediction of machines. IEEE/ASME Transactions on Mechatronics, 28(1), 350-359. doi: 10.1109/TMECH.2022.3195524
Haris, M., Hasan, M. N., Jahanzeb Hussain Pirzada, S., & Qin, S. (2020). Bayesian optimized long-short term memory recurrent neural network for prognostics of thermally aged power mosfets [Conference paper]. In S. M., M. M.A., & N. S. (Eds.), . Institute of Electrical and Electronics Engineers Inc. (Cited by: 2) doi: 10.1109/RAEECS50817.2020.9265738
Hestness, J., Narang, S., Ardalani, N., Diamos, G. F., Jun, H., Kianinejad, H., . . . Zhou, Y. (2017). Deep learning scaling is predictable, empirically. CoRR, abs/1712.00409. Retrieved from http://arxiv.org/abs/1712.00409
Hochreiter, S., & Schmidhuber, J. (1997). Long short-term memory. Neural computation, 9(8), 1735–1780.
Huang, C.-G., Zhu, J., Han, Y., & Peng, W. (2022). A novel bayesian deep dual network with unsupervised domain adaptation for transfer fault prognosis across different machines. IEEE Sensors Journal, 22(8), 7855-7867. doi: 10.1109/JSEN.2021.3133622
Kamei, S., & Taghipour, S. (2023). A comparison study of centralized and decentralized federated learning approaches utilizing the transformer architecture for estimating remaining useful life [Article]. Reliability Engineering and System Safety, 233. (Cited by: 6) doi: 10.1016/j.ress.2023.109130
Kingma, D. P., & Ba, J. (2015). Adam: A method for stochastic optimization. In Y. Bengio & Y. LeCun (Eds.), 3rd international conference on learning representations, ICLR 2015, san diego, ca, usa, may 7-9, 2015, conference track proceedings. Retrieved from http://arxiv.org/abs/1412.6980
Lallart, M., Wu, B., Li, W., & Qiu, M.-q. (2017). Remaining useful life prediction of bearing with vibration signals based on a novel indicator. Shock and Vibration, 2017, 8927937. Retrieved from https://doi.org/10.1155/2017/8927937 doi: 10.1155/2017/8927937
LeCun, Y., Bengio, Y., & Hinton, G. (2015). Deep learning. Nature, 521(7553), 436–444. Retrieved from https://doi.org/10.1038/nature14539 doi: 10.1038/nature14539
Li, Q., Wen, Z., Wu, Z., Hu, S., Wang, N., Li, Y., . . . He, B. (2023, apr). A survey on federated learning systems: Vision, hype and reality for data privacy and protection. IEEE Transactions on Knowledge; Data Engineering, 35(04), 3347-3366. doi: 10.1109/TKDE.2021.3124599
Li, T. (2024). Particle filter-based fatigue damage prognosis using prognostic-aided model updating. Mechanical Systems and Signal Processing, 211, 111244. Retrieved from https://www.sciencedirect.com/science/article/pii/S0888327024001420 doi: https://doi.org/10.1016/j.ymssp.2024.111244
Liaw, R., Liang, E., Nishihara, R., Moritz, P., Gonzalez, J. E., & Stoica, I. (2018). Tune: A research platform for distributed model selection and training. arXiv preprint arXiv:1807.05118.
Loshchilov, I., & Hutter, F. (2019). Decoupled weight decay regularization. In International conference on learning representations. Retrieved from https://openreview.net/forum?id=Bkg6RiCqY7
Mahamad, A. K., Saon, S., & Hiyama, T. (2010). Predicting remaining useful life of rotating machinery based artificial neural network. Computers & Mathematics with Applications, 60(4), 1078-1087. Retrieved from https://www.sciencedirect.com/science/article/pii/S0898122110002555 (PCO’ 2010) doi: https://doi.org/10.1016/j.camwa.2010.03.065
McMahan, B., Moore, E., Ramage, D., Hampson, S., & Arcas, B. A. y. (2017, 20–22 Apr). Communication-efficient learning of deep networks from decentralized data. In A. Singh & J. Zhu (Eds.), Proceedings of the 20th international conference on artificial intelligence and statistics (Vol. 54, pp. 1273–1282). PMLR. Retrieved from https://proceedings.mlr.press/v54/mcmahan17a.html
Meriem, H., Nora, H., & Samir, O. (2023). Predictive maintenance for smart industrial systems: A roadmap. Procedia Computer Science, 220, 645-650. Retrieved from https://www.sciencedirect.com/science/article/pii/S1877050923006178 (The 14th International Conference on Ambient Systems, Networks and Technologies Networks (ANT) and The 6th International Conference on Emerging Data and Industry 4.0 (EDI40)) doi: https://doi.org/10.1016/j.procs.2023.03.082
Moshawrab, M., Adda, M., Bouzouane, A., Ibrahim, H., & Raad, A. (2023). Reviewing federated learning aggregation algorithms; strategies, contributions, limitations and future perspectives. Electronics, 12(10). Retrieved from https://www.mdpi.com/2079-9292/12/10/2287 doi: 10.3390/electronics12102287
Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., . . . Chintala, S. (2019). Pytorch: An imperative style, high-performance deep learning library. In Advances in neural information processing systems 32 (pp. 8024–8035). Curran Associates, Inc. Retrieved from http://papers.neurips.cc/paper/9015-pytorch-an-imperative-style-high-performance-deep-learning-library.pdf
Pecht, M., & Kang, M. (2018). Prognostics and health management of electronics : fundamentals, machine learning, and internet of things (Second edition. ed.). Hoboken, New Jersey: John Wiley & Sons.
Ren, H., Du, X., Yu, Y., Wang, J., Zhou, J., & Peng, Y. (2022). Power mosfet lifetime prediction method based on optimized long short-term memory neural network [Conference paper]. Institute of Electrical and Electronics Engineers Inc. (Cited by: 0) doi: 10.1109/ECCE50734.2022.9947640
Sahu, A. K., Li, T., Sanjabi, M., Zaheer, M., Talwalkar, A., & Smith, V. (2018). On the convergence of federated optimization in heterogeneous networks. CoRR, abs/1812.06127. Retrieved from http://arxiv.org/abs/1812.06127
Saxena, A., Goebel, K., Simon, D., & Eklund, N. (2008). Damage propagation modeling for aircraft engine run-to-failure simulation. In 2008 international conference on prognostics and health management (p. 1-9). doi: 10.1109/PHM.2008.4711414
Singh, J., Darpe, A. K., & Singh, S. P. (2020, may). Bearing remaining useful life estimation using an adaptive data-driven model based on health state change point identification and k-means clustering. Measurement Science and Technology, 31(8), 085601. Retrieved from https://dx.doi.org/10.1088/1361-6501/ab6671 doi: 10.1088/1361-6501/ab6671
Söderkvist Vermelin, W., L¨ovberg, A., Misiorny, M., P. Eng, M., & Brinkfeldt, K. (2023). Data-driven remaining useful life estimation of discrete power electronic devices. In Proceedings of the 33rd european safety and reliability conference (esrel 2023) (Vol. 33). European Safety and Reliability Conference. Retrieved from https://urn.kb.se/resolve?urn=urn:nbn:se:ri:diva-67109 doi: 10.3850/978-981-18-8071-1-4procd
Tian, Z. (2009). An artificial neural network approach for remaining useful life prediction of equipments subject to condition monitoring. In 2009 8th international conference on reliability, maintainability and safety (p. 143-148). doi: 10.1109/ICRMS.2009.5270220
Tian, Z., Wong, L., & Safaei, N. (2010). A neural network approach for remaining useful life prediction utilizing both failure and suspension histories. Mechanical Systems and Signal Processing, 24(5), 1542-1555. Retrieved from https://www.sciencedirect.com/science/article/pii/S088832700900377X (Special Issue: Operational Modal Analysis) doi: https://doi.org/10.1016/j.ymssp.2009.11.005
Van Rossum, G., & Drake, F. L. (2009). Python 3 reference manual. Scotts Valley, CA: CreateSpace.
Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A. N., . . . Polosukhin, I. (2017). Attention is all you need. In I. Guyon et al. (Eds.), Advances in neural information processing systems (Vol. 30). Curran Associates, Inc. Retrieved from https://proceedings.neurips.cc/paper files/paper/2017/file/3f5ee243547dee91fbd053c1c4a845aa-Paper.pdf
Vibhorpandhare, Jia, X., & Lee, J. (2021). Collaborative prognostics for machine fleets using a novel federated baseline learner.. Retrieved from https://api.semanticscholar.org/CorpusID:244645140
Wang, J., Wen, G., Yang, S., & Liu, Y. (2019). Remaining useful life estimation in prognostics using deep bidirectional lstm neural network [Conference paper]. In D. P., L. C., Y. S., D. P., & S. R.-V. (Eds.), (p. 1037 – 1042). Institute of Electrical and Electronics Engineers Inc. (Cited by: 121) doi: 10.1109/PHM-Chongqing.2018.00184
Wu, S.-j., Gebraeel, N., Lawley, M. A., & Yih, Y. (2007). A neural network integrated decision support system for condition-based optimal predictive maintenance policy. IEEE Transactions on Systems, Man, and Cybernetics - Part A: Systems and Humans, 37(2), 226-236. doi: 10.1109/TSMCA.2006.886368
Xia, M., Li, T., Shu, T., Wan, J., de Silva, C. W., & Wang, Z. (2019). A two-stage approach for the remaining useful life prediction of bearings using deep neural networks. IEEE Transactions on Industrial Informatics, 15(6), 3703-3711. doi: 10.1109/TII.2018.2868687
Xu, J., Duan, S., Chen, W., Wang, D., & Fan, Y. (2022). Sacgnet: A remaining useful life prediction of bearing with self-attention augmented convolution gru network. Lubricants, 10(2). Retrieved from https://www.mdpi.com/2075-4442/10/2/21 doi: 10.3390/lubricants10020021
Zhang, J.,Wang, P., Yan, R., & Gao, R. X. (2018). Long short-term memory for machine remaining life prediction [Article]. Journal of Manufacturing Systems, 48, 78 – 86. (Cited by: 302) doi: 10.1016/j.jmsy.2018.05.011
Zheng, S., Ristovski, K., Farahat, A., & Gupta, C. (2017). Long short-term memory network for remaining useful life estimation. In 2017 ieee international conference on prognostics and health management (icphm) (p. 88-95). doi: 10.1109/ICPHM.2017.7998311
Zhu, J., Chen, N., & Shen, C. (2020). A new data-driven transferable remaining useful life prediction approach for bearing under different working conditions. Mechanical Systems and Signal Processing, 139, 106602. Retrieved from https://www.sciencedirect.com/science/article/pii/S0888327019308234 doi: https://doi.org/10.1016/j.ymssp.2019.106602
Section
Technical Papers