A federated data fusion-based prognostic model for applications with multi-stream incomplete signals

References

• Bagheri, B., Rezapoor, M. and Lee, J. (2020) A unified data security framework for federated prognostics and health management in smart manufacturing. Manufacturing Letters, 24, 136–139.
   Google Scholar
• Balzano, L., Nowak, R. and Recht, B. (2010) Online identification and tracking of subspaces from highly incomplete information, in 2010 48th Annual Allerton Conference on Communication, Control, and Computing (Allerton), IEEE Press, Piscataway, NJ, pp. 704–711.
   Google Scholar
• Balzano, L., Recht, B. and Nowak, R. (2010) High-dimensional matched subspace detection when data are missing, in 2010 IEEE International Symposium on Information Theory, IEEE Press, Piscataway, NJ, pp. 1638–1642.
   Google Scholar
• Balzano, L. and Wright, S.J. (2013) On Grouse and incremental SVD, in 2013 5th IEEE International Workshop on Computational Advances in Multi-Sensor Adaptive Processing (CAMSAP), IEEE Press, Piscataway, NJ, pp. 1–4.
   Google Scholar
• Brand, M. (2002) Incremental singular value decomposition of uncertain data with missing values, in Computer Vision-ECCV 2002: 7th European Conference on Computer Vision Copenhagen, Denmark, May 28–31, 2002 Proceedings, Part I 7, Springer, Berlin, pp. 707–720.
   Google Scholar
• Briggs, C., Fan, Z. and Andras, P. (2021) A review of privacy-preserving federated learning for the internet-of-things. Federated Learning Systems, 2350, 21–50.
   Google Scholar
• Bunch, J.R. and Nielsen, C.P. (1978) Updating the singular value decomposition. Numerische Mathematik, 31(2), 111–129.
   Web of Science ®Google Scholar
• Chang, K., Balachandar, N., Lam, C., Yi, D., Brown, J., Beers, A., Rosen, B., Rubin, D.L. and Kalpathy-Cramer, J. (2018) Distributed deep learning networks among institutions for medical imaging. Journal of the American Medical Informatics Association, 25(8), 945–954.
   PubMed Web of Science ®Google Scholar
• Chen, X., Wang, H., Lu, S. and Yan, R. (2023) Bearing remaining useful life prediction using federated learning with Taylor-expansion network pruning. IEEE Transactions on Instrumentation and Measurement, 72, 1–10.
   PubMed Web of Science ®Google Scholar
• Chik, W.B. (2013) The Singapore personal data protection act and an assessment of future trends in data privacy reform. Computer Law & Security Review, 29(5), 554–575.
   Web of Science ®Google Scholar
• Chung, S. and Al Kontar, R. (2023) Federated condition monitoring signal prediction with improved generalization. IEEE Transactions on Reliability, 73(1), 438–450.
   Web of Science ®Google Scholar
• Dai, W. and Milenkovic, O. (2010) Set: An algorithm for consistent matrix completion, in 2010 IEEE International Conference on Acoustics, Speech and Signal Processing, IEEE Press, Piscataway, NJ, pp. 3646–3649.
   Google Scholar
• Doray, L. (1994) Ibnr reserve under a loglinear location-scale regression model. Casualty Actuarial Society Forum, 2, 607–652.
   Google Scholar
• Fang, X., Gebraeel, N.Z. and Paynabar, K. (2017) Scalable prognostic models for large-scale condition monitoring applications. IISE Transactions, 49(7), 698–710.
   Web of Science ®Google Scholar
• Fang, X., Paynabar, K. and Gebraeel, N. (2017) Multistream sensor fusion-based prognostics model for systems with single failure modes. Reliability Engineering & System Safety, 159, 322–331.
   Web of Science ®Google Scholar
• Fang, X., Yan, H., Gebraeel, N. and Paynabar, K. (2021) Multi-sensor prognostics modeling for applications with highly incomplete signals. IISE Transactions, 53(5), 597–613.
   Web of Science ®Google Scholar
• Fang, X., Zhou, R. and Gebraeel, N. (2015) An adaptive functional regression-based prognostic model for applications with missing data. Reliability Engineering & System Safety, 133, 266–274.
   Web of Science ®Google Scholar
• Gao, Y., Wen, Y. and Wu, J. (2021) A neural network-based joint prognostic model for data fusion and remaining useful life prediction. IEEE Transactions on Neural Networks and Learning Systems, 32(1), 117–127.
   PubMed Web of Science ®Google Scholar
• Gebraeel, N., Elwany, A. and Pan, J. (2009) Residual life predictions in the absence of prior degradation knowledge. IEEE Transactions on Reliability, 58(1), 106–117.
   Web of Science ®Google Scholar
• Gebraeel, N., Lawley, M., Liu, R. and Parmeshwaran, V. (2004) Residual life predictions from vibration-based degradation signals: A neural network approach. IEEE Transactions on Industrial Electronics, 51(3), 694–700.
   Web of Science ®Google Scholar
• Gebraeel, N.Z. and Lawley, M.A. (2008) A neural network degradation model for computing and updating residual life distributions. IEEE Transactions on Automation Science and Engineering, 5(1), 154–163.
   Web of Science ®Google Scholar
• Guo, L., Yu, Y., Qian, M., Zhang, R., Gao, H. and 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.
   Web of Science ®Google Scholar
• Karhunen, K. (1947) Ueber lineare methoden in der wahrscheinlichkeitsrechnung issue 37 of annales academiae scientiarum fennicae. Series A 1.
   Google Scholar
• Keshavan, R.H., Montanari, A. and Oh, S. (2010) Matrix completion from a few entries. IEEE Transactions on Information Theory, 56(6), 2980–2998.
   Web of Science ®Google Scholar
• Konečnỳ, J., McMahan, H.B., Ramage, D. and Richtárik, P. (2016) Federated optimization: Distributed machine learning for on-device intelligence. arXiv preprint arXiv:1610.02527.
   Google Scholar
• Konečnỳ, J., McMahan, H.B., Yu, F.X., Richtárik, P., Suresh, A.T. and Bacon, D. (2016) Federated learning: Strategies for improving communication efficiency. arXiv preprint arXiv:1610.05492.
   Google Scholar
• Kontar, R., Shi, N., Yue, X., Chung, S., Byon, E., Chowdhury, M., Jin, J., Kontar, W., Masoud, N., Nouiehed, M. et al. (2021) The internet of federated things (ioft). IEEE Access, 9, 156071–156113.
   Google Scholar
• Li, L., Fan, Y., Tse, M. and Lin, K.-Y. (2020) A review of applications in federated learning. Computers & Industrial Engineering, 106854.
   Google Scholar
• Li, Q., Wen, Z., Wu, Z., Hu, S., Wang, N., Li, Y., Liu, X. and He, B. (2019) A survey on federated learning systems: vision, hype and reality for data privacy and protection. arXiv preprint arXiv:1907.09693.
   Google Scholar
• Li, T., Sahu, A.K., Talwalkar, A. and Smith, V. (2020) Federated learning: Challenges, methods, and future directions. IEEE Signal Processing Magazine, 37(3), 50–60.
   Web of Science ®Google Scholar
• Liu, K., Gebraeel, N.Z. and Shi, J. (2013) A data-level fusion model for developing composite health indices for degradation modeling and prognostic analysis. IEEE Transactions on Automation Science and Engineering, 10(3), 652–664.
   Web of Science ®Google Scholar
• McMahan, B., Moore, E., Ramage, D., Hampson, S. and Arcas, B.A.y (2017) Communication-efficient learning of deep networks from decentralized data, in Artificial Intelligence and Statistics, pp. 1273–1282.
   Google Scholar
• McMahan, H.B., Moore, E., Ramage, D. and Arcas, B.A.y (2016) Federated learning of deep networks using model averaging. arXiv preprint arXiv:1602.05629.
   Google Scholar
• Meeker, W. and Escobar, L. (1998) Statistical Methods for Reliability Data, John Wiley & Sons New York.
   Google Scholar
• Pardau, S.L. (2018) The California consumer privacy act: Towards a european-style privacy regime in the United States. Technology Law & Policy, 23, 68–104.
   Google Scholar
• Qu, L., Zhou, Y., Liang, P.P., Xia, Y., Wang, F., Adeli, E., Fei-Fei, L. and Rubin, D. (2022) Rethinking architecture design for tackling data heterogeneity in federated learning, in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, IEEE, New Orleans, LA, pp. 10061–10071.
   Google Scholar
• Saxena, A., Goebel, K., Simon, D. and Eklund, N. (2008) Damage propagation modeling for aircraft engine run-to-failure simulation, in 2008 International Conference on Prognostics and Health Management, IEEE Press, Piscataway, NJ, pp. 1–9.
   Google Scholar
• Sikorska, J.Z., Hodkiewicz, M. and Ma, L. (2011) Prognostic modelling options for remaining useful life estimation by industry. Mechanical Systems and Signal Processing, 25(5), 1803–1836.
   Web of Science ®Google Scholar
• Su, Y. and Fang, X. (2022) A two-stage federated learning approach for industrial prognostics using high-dimensional signals.
   Google Scholar
• Voigt, P. and Von dem Bussche, A. (2017) The EU General Data Protection Regulation (gdpr). A Practical Guide, 1st Ed., Cham: Springer International Publishing 10, 3152676.
   Google Scholar
• Wu, J.-Y., Wu, M., Chen, Z., Li, X.-L. and Yan, R. (2021) Degradation-aware remaining useful life prediction with lstm autoencoder. IEEE Transactions on Instrumentation and Measurement, 70, 1–10.
   PubMed Web of Science ®Google Scholar
• Yang, Q., Liu, Y., Chen, T. and Tong, Y. (2019) Federated machine learning: Concept and applications. ACM Transactions on Intelligent Systems and Technology (TIST), 10(2), 1–19.
   Web of Science ®Google Scholar
• Yao, F., Müller, H.-G. and Wang, J.-L. (2005) Functional data analysis for sparse longitudinal data. Journal of the American Statistical Association, 100(470), 577–590.
   Web of Science ®Google Scholar
• Zhou, C. and Fang, X. (2023) A supervised tensor dimension reduction-based prognostic model for applications with incomplete imaging data. INFORMS Journal on Data Science.
   Google Scholar
• Zhou, C., Su, Y., Xia, T. and Fang, X. (2023) Federated multilinear principal component analysis with applications in prognostics. arXiv preprint arXiv:2312.06050.
   Google Scholar