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What is Machine Learning in Rotating Machinery Fault Analysis about?

This review article discusses the challenges and advancements in intelligent fault diagnosis and prognosis (IFDP) models for rotating machinery, particularly in the context of Industry 4.0. It highlights the importance of machine learning techniques in addressing various issues related to fault analysis, including model assessment, data sources, and compound faults. The study also identifies gaps in existing literature and proposes future research directions to enhance the effectiveness of IFDP approaches.

Author

hridinpradeep2

Language

EN