What question did this study set out to answer?

To comprehensively review recent progress and methodologies in data-driven deep learning models for vibration-based fault diagnosis and condition monitoring in rotating industrial machinery.

October 9, 2022Artificial Intelligence ReviewOpen Access

Recent advances in the application of deep learning for fault diagnosis of rotating machinery using vibration signals

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Authors

BTBayu Adhi TamaUniversity of Maryland, Baltimore County MVMalinda VaniaUlsan National Institute of Science and Technology SLSeung‐Chul LeeGoddard Space Flight Center

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Implication

Systematic review synthesizes deep learning applications for vibration-based fault diagnosis in rotating machinery, highlighting emerging trends like physics-informed machine learning.

Key Points

To comprehensively review recent progress and methodologies in data-driven deep learning models for vibration-based fault diagnosis and condition monitoring in rotating industrial machinery.
Searched two major indexing databases, Web of Science and Scopus, for recent literature on vibration-based machine fault diagnosis.
Identified and selected 59 relevant studies for systematic review and synthesis of deep learning architectures.
Deep learning frameworks demonstrate strong performance in extracting diagnostic features from vibration signals to detect both immediate faults and ongoing wear.
Identified prominent emerging research directions, specifically graph-based neural networks, physics-informed machine learning, and hybrid transformer-convolutional network architectures.

Cite This Study

Tama et al. (2022) studied this question.

synapsesocial.com/papers/69d960f7da3af5b1d0836538 https://doi.org/10.1007/s10462-022-10293-3

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Implication

Systematic review synthesizes deep learning applications for vibration-based fault diagnosis in rotating machinery, highlighting emerging trends like physics-informed machine learning.

Recent advances in the application of deep learning for fault diagnosis of rotating machinery using vibration signals

Discussion

Member takes

Implication

Key Points

Cite This Study

Recent advances in the application of deep learning for fault diagnosis of rotating machinery using vibration signals

Discussion

Member takes

Implication

Key Points

Cite This Study

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