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The machine learning paradigms discussed in previous chapters, including supervised learning (Chaps. 3 , 4 , and 5 ), unsupervised learning (Chap. 6 ), prognostics (Chap. 7 ), and advanced strategies like transfer learning and federated learning (Chap. 8 ), have significantly advanced the capabilities of Prognostics and Health Management (PHM) systems. They enable more accurate fault diagnosis, RUL prediction, and anomaly detection by learning complex patterns from vibration data.

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Emerging Frontiers: Reinforcement Learning and Online/Continual Learning in PHM

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  • 发表日期：

    2026年

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  • Baris Aykent

摘要

The machine learning paradigms discussed in previous chapters, including supervised learning (Chaps. 3 , 4 , and 5 ), unsupervised learning (Chap. 6 ), prognostics (Chap. 7 ), and advanced strategies like transfer learning and federated learning (Chap. 8 ), have significantly advanced the capabilities of Prognostics and Health Management (PHM) systems. They enable more accurate fault diagnosis, RUL prediction, and anomaly detection by learning complex patterns from vibration data.