# Data-driven Machinery Fault Diagnosis: A Comprehensive Review

arXiv: https://arxiv.org/abs/2405.18843 · HTML: https://mlsanity.com/paper/2405.18843/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- Builds on 12 of its 404 references that are in the map.
- First posted on arXiv 29 May 2024.

## Builds on (most cited first, 12 of 12)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Improved Techniques for Training GANs](https://mlsanity.com/paper/1606.03498/) (2016)
- [Unsupervised Anomaly Detection with Generative Adversarial Networks to Guide Marker Discovery](https://mlsanity.com/paper/1703.05921/) (2017)
- [Deep Learning for Anomaly Detection: A Review](https://mlsanity.com/paper/2007.02500/) (2020)
- [Generative Adversarial Networks (GANs Survey): Challenges, Solutions, and Future Directions](https://mlsanity.com/paper/2005.00065/) (2020)
- [Offline Meta-Reinforcement Learning for Industrial Insertion](https://mlsanity.com/paper/2110.04276/) (2021)
- [A Novel GAN-based Fault Diagnosis Approach for Imbalanced Industrial Time Series](https://mlsanity.com/paper/1904.00575/) (2019)
- [Pre-Processing-Free Gear Fault Diagnosis Using Small Datasets with Deep Convolutional Neural Network-Based Transfer Learning](https://mlsanity.com/paper/1710.08904/) (2017)
- [Deep Learning Algorithms for Rotating Machinery Intelligent Diagnosis: An Open Source Benchmark Study](https://mlsanity.com/paper/2003.03315/) (2020)
- [Wasserstein Distance based Deep Adversarial Transfer Learning for Intelligent Fault Diagnosis](https://mlsanity.com/paper/1903.06753/) (2019)
- [A comprehensive review on convolutional neural network in machine fault diagnosis](https://mlsanity.com/paper/2002.07605/) (2020)
- [Machine Learning and Deep Learning Algorithms for Bearing Fault Diagnostics -- A Comprehensive Review](https://mlsanity.com/paper/1901.08247/) (2019)

## Built on by (most cited first, 7 of 7)

- [Ensemble-Enhanced Graph Autoencoder with GAT and Transformer-Based Encoders for Robust Fault Diagnosis](https://mlsanity.com/paper/2504.09427/) (2025)
- [Enhancing Hardware Fault Tolerance in Machines with Reinforcement Learning Policy Gradient Algorithms](https://mlsanity.com/paper/2407.15283/) (2024)
- [Finding One's Bearings in the Hyperparameter Landscape of a Wide-Kernel Convolutional Fault Detector](https://mlsanity.com/paper/2411.15191/) (2024)
- [Bearing fault diagnosis based on multi-scale spectral images and convolutional neural network](https://mlsanity.com/paper/2503.21566/) (2025)
- [Pad'e Approximant Neural Networks for Enhanced Electric Motor Fault Diagnosis Using Vibration and Acoustic Data](https://mlsanity.com/paper/2507.02599/) (2025)
- [Label-free Industrial Fault Detection via Adversarial Inverse Reinforcement Learning: A System for Run-to-Failure Prognostics](https://mlsanity.com/paper/2607.22987/) (2026)
- [Cross-Domain Industrial Fault Detection by Causal Mechanism Monitoring](https://mlsanity.com/paper/2608.14666/) (2026)

## 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.