mlsanityData to 7 Oct 2026

Data-driven Machinery Fault Diagnosis: A Comprehensive Review

arXiv 2405.18843 ↗First posted 29 May 2024cs.AI cs.LGcited by 7

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Abstract (arXiv)

In this era of advanced manufacturing, it's now more crucial than ever to diagnose machine faults as early as possible to guarantee their safe and efficient operation. With the massive surge in industrial big data and advancement in sensing and computational technologies, data-driven Machinery Fault Diagnosis (MFD) solutions based on machine/deep learning approaches have been used ubiquitously in manufacturing. Timely and accurately identifying faulty machine signals is vital in industrial applications for which many relevant solutions have been proposed and are reviewed in many articles. Despite the availability of numerous solutions and reviews on MFD, existing works often lack several aspects. Most of the available literature has limited applicability in a wide range of manufacturing settings due to their concentration on a particular type of equipment or method of analysis. Additionally, discussions regarding the challenges associated with implementing data-driven approaches, such as dealing with noisy data, selecting appropriate features, and adapting models to accommodate new or unforeseen faults, are often superficial or completely overlooked. Thus, this survey provides a comprehensive review of the articles using different types of machine learning approaches for the detection and diagnosis of various types of machinery faults, highlights their strengths and limitations, provides a review of the methods used for condition-based analyses, comprehensively discusses the available machinery fault datasets, introduces future researchers to the possible challenges they have to encounter while using these approaches for MFD and recommends the probable solutions to mitigate those problems. The future research prospects are also pointed out for a better understanding of the field. We believe this article will help researchers and contribute to the further development of the field.

Key facts. Cited by 7 papers in the map (data through 7 Oct 2026). Builds on 12 of its 404 references that are in the map. First posted on arXiv 29 May 2024.

Builds on

12 of 404 references.

References · 12

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Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2024: 2; 2025: 3; 2026: 2.

7 papers.

  1. 2025Ensemble-Enhanced Graph Autoencoder with GAT and Transformer-Based Encoders for Robust Fault Diagnosiscited by 1
  2. 2024Enhancing Hardware Fault Tolerance in Machines with Reinforcement Learning Policy Gradient Algorithmscited by 0
  3. 2024Finding One's Bearings in the Hyperparameter Landscape of a Wide-Kernel Convolutional Fault Detectorcited by 0
  4. 2025Bearing fault diagnosis based on multi-scale spectral images and convolutional neural networkcited by 0
  5. 2025Pad'e Approximant Neural Networks for Enhanced Electric Motor Fault Diagnosis Using Vibration and Acoustic Datacited by 0
  6. 2026Label-free Industrial Fault Detection via Adversarial Inverse Reinforcement Learning: A System for Run-to-Failure Prognosticscited by 0
  7. 2026Cross-Domain Industrial Fault Detection by Causal Mechanism Monitoringcited by 0