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The preceding chapters have laid a strong foundation in applying various machine learning techniques—from traditional supervised methods to deep learning architectures like CNNs and LSTMs, as well as unsupervised approaches—to vibration data for diagnostics and prognostics. However, many of these methods operate under certain ideal assumptions, such as the availability of large, representative, and well-labeled datasets for the specific machine and operating conditions of interest. In real-world industrial settings, these assumptions often do not hold true, leading to significant challenges in developing robust and generalizable PHM solutions.

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Advanced Topics: Transfer Learning and Federated Learning in PHM

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  • 发表日期：

    2026年

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  • Baris Aykent

摘要

The preceding chapters have laid a strong foundation in applying various machine learning techniques—from traditional supervised methods to deep learning architectures like CNNs and LSTMs, as well as unsupervised approaches—to vibration data for diagnostics and prognostics. However, many of these methods operate under certain ideal assumptions, such as the availability of large, representative, and well-labeled datasets for the specific machine and operating conditions of interest. In real-world industrial settings, these assumptions often do not hold true, leading to significant challenges in developing robust and generalizable PHM solutions.