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A Federated Data Fusion-Based Prognostic Model for Applications with Multi-Stream Incomplete Signals (2311.07474v2)

Published 13 Nov 2023 in stat.ML, cs.LG, eess.SP, and stat.ME

Abstract: Most prognostic methods require a decent amount of data for model training. In reality, however, the amount of historical data owned by a single organization might be small or not large enough to train a reliable prognostic model. To address this challenge, this article proposes a federated prognostic model that allows multiple users to jointly construct a failure time prediction model using their multi-stream, high-dimensional, and incomplete data while keeping each user's data local and confidential. The prognostic model first employs multivariate functional principal component analysis to fuse the multi-stream degradation signals. Then, the fused features coupled with the times-to-failure are utilized to build a (log)-location-scale regression model for failure prediction. To estimate parameters using distributed datasets and keep the data privacy of all participants, we propose a new federated algorithm for feature extraction. Numerical studies indicate that the performance of the proposed model is the same as that of classic non-federated prognostic models and is better than that of the models constructed by each user itself.

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References (43)
  1. Residual life predictions from vibration-based degradation signals: a neural network approach. IEEE Transactions on industrial electronics, 51(3):694–700, 2004.
  2. Prognostic modelling options for remaining useful life estimation by industry. Mechanical systems and signal processing, 25(5):1803–1836, 2011.
  3. Federated multilinear principal component analysis with applications in prognostics. arXiv preprint arXiv:2312.06050, 2023.
  4. 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, 2021.
  5. A neural network degradation model for computing and updating residual life distributions. IEEE Transactions on Automation Science and Engineering, 5(1):154–163, 2008.
  6. Residual life predictions in the absence of prior degradation knowledge. IEEE Transactions on Reliability, 58(1):106–117, 2009.
  7. Scalable prognostic models for large-scale condition monitoring applications. IISE Transactions, 49(7):698–710, 2017.
  8. Multistream sensor fusion-based prognostics model for systems with single failure modes. Reliability Engineering & System Safety, 159:322–331, 2017.
  9. A supervised tensor dimension reduction-based prognostic model for applications with incomplete imaging data. INFORMS Journal on Data Science, 2023.
  10. Stuart L Pardau. The california consumer privacy act: towards a european-style privacy regime in the united states. J. Tech. L. & Pol’y, 23:68, 2018.
  11. Paul Voigt and Axel Von dem Bussche. The eu general data protection regulation (gdpr). A Practical Guide, 1st Ed., Cham: Springer International Publishing, 10:3152676, 2017.
  12. Warren B Chik. The singapore personal data protection act and an assessment of future trends in data privacy reform. Computer Law & Security Review, 29(5):554–575, 2013.
  13. The internet of federated things (ioft). IEEE Access, 9:156071–156113, 2021.
  14. Federated optimization: Distributed machine learning for on-device intelligence. arXiv preprint arXiv:1610.02527, 2016.
  15. Federated learning: Strategies for improving communication efficiency. arXiv preprint arXiv:1610.05492, 2016.
  16. Federated learning of deep networks using model averaging.(2016). arXiv preprint arXiv:1602.05629, 2016.
  17. A survey on federated learning systems: vision, hype and reality for data privacy and protection. arXiv preprint arXiv:1907.09693, 2019.
  18. Federated machine learning: Concept and applications. ACM Transactions on Intelligent Systems and Technology (TIST), 10(2):1–19, 2019.
  19. A review of applications in federated learning. Computers & Industrial Engineering, page 106854, 2020.
  20. Federated learning: Challenges, methods, and future directions. IEEE Signal Processing Magazine, 37(3):50–60, 2020.
  21. A review of privacy-preserving federated learning for the internet-of-things. Federated Learning Systems, pages 21–50, 2021.
  22. A unified data security framework for federated prognostics and health management in smart manufacturing. Manufacturing Letters, 24:136–139, 2020.
  23. Bearing remaining useful life prediction using federated learning with taylor-expansion network pruning. IEEE Transactions on Instrumentation and Measurement, 72:1–10, 2023.
  24. Degradation-aware remaining useful life prediction with lstm autoencoder. IEEE Transactions on Instrumentation and Measurement, 70:1–10, 2021.
  25. Federated condition monitoring signal prediction with improved generalization. IEEE Transactions on Reliability, pages 1–13, 2023.
  26. 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, 2023.
  27. Fedrul: A new federated learning method for edge-cloud collaboration based remaining useful life prediction of machines. IEEE/ASME Transactions on Mechatronics, 2022.
  28. Federated (log)-location-scale regression. 2022.
  29. 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, 2013.
  30. Louis Doray. Ibnr reserve under a loglinear location-scale regression model. In Casualty Actuarial Society Forum, volume 2, pages 607–652. Citeseer, 1994.
  31. An adaptive functional regression-based prognostic model for applications with missing data. Reliability Engineering & System Safety, 133:266–274, 2015.
  32. Multi-sensor prognostics modeling for applications with highly incomplete signals. IISE Transactions, 53(5):597–613, 2021.
  33. WQ Meeker and LA Escobar. Statistical methods for reliability data john wiley & sons new york. New York, 1998.
  34. K Karhunen. Ueber lineare methoden in der wahrscheinlichkeitsrechnung issue 37 of annales academiae scientiarum fennicae. Series A, 1, 1947.
  35. Functional data analysis for sparse longitudinal data. Journal of the American statistical association, 100(470):577–590, 2005.
  36. Updating the singular value decomposition. Numerische Mathematik, 31(2):111–129, 1978.
  37. On grouse and incremental svd. In 2013 5th IEEE international workshop on computational advances in multi-sensor adaptive processing (CAMSAP), pages 1–4. IEEE, 2013.
  38. Matthew Brand. 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, pages 707–720. Springer, 2002.
  39. Online identification and tracking of subspaces from highly incomplete information. In 2010 48th Annual allerton conference on communication, control, and computing (Allerton), pages 704–711. IEEE, 2010.
  40. Set: An algorithm for consistent matrix completion. In 2010 IEEE International Conference on Acoustics, Speech and Signal Processing, pages 3646–3649. IEEE, 2010.
  41. Matrix completion from a few entries. IEEE transactions on information theory, 56(6):2980–2998, 2010.
  42. High-dimensional matched subspace detection when data are missing. In 2010 IEEE International Symposium on Information Theory, pages 1638–1642. IEEE, 2010.
  43. Damage propagation modeling for aircraft engine run-to-failure simulation. In 2008 international conference on prognostics and health management, pages 1–9. IEEE, 2008.

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