Technology Category
Machine Learning: Transfer, Domain Adaptation, Multi-Task LearningKnowledge Representation and Reasoning: Diagnosis and Abductive ReasoningMultiagent Systems: Other Foundations of Multi Agent Systems
Data-driven machinery fault diagnosis: A comprehensive review
🤖 AI Summary
Existing data-driven mechanical fault diagnosis (MFD) research lacks a systematic analysis of industrial deployment challenges—particularly regarding noise robustness, feature transferability, and adaptability to unknown faults. To address this gap, this paper proposes the first comprehensive, multi-dimensional taxonomy covering signal processing, feature learning, model generalization, and industrial deployment, enabling unified evaluation of deep learning, graph neural networks, and physics-informed methods across their applicability boundaries. The survey systematically reviews key techniques—including time-frequency analysis, 1D-CNNs, LSTMs, Transformers, self-supervised pretraining, and digital twin–assisted diagnosis—and synthesizes optimal method combinations for twelve representative fault scenarios. It identifies three critical bottlenecks hindering real-world adoption: few-shot learning, cross-operating-condition transfer, and model interpretability.
Technology Category
Application Category
Problem
Research questions and friction points this paper is trying to address.
Comprehensive review of machine learning approaches
Challenges in data-driven machinery fault diagnosis
Future research prospects in fault detection
Innovation
Methods, ideas, or system contributions that make the work stand out.
Machine learning for fault detection
Deep learning in industrial diagnostics
Comprehensive review of fault datasets
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