# Machine Learning and Deep Learning Algorithms for Bearing Fault Diagnostics -- A Comprehensive Review

arXiv: https://arxiv.org/abs/1901.08247 · HTML: https://mlsanity.com/paper/1901.08247/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 24 Jan 2019.

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

- [Continual Learning for Predictive Maintenance: Overview and Challenges](https://mlsanity.com/paper/2301.12467/) (2023)
- [Data-driven Machinery Fault Diagnosis: A Comprehensive Review](https://mlsanity.com/paper/2405.18843/) (2024)
- [Zero-Shot Motor Health Monitoring by Blind Domain Transition](https://mlsanity.com/paper/2212.06154/) (2022)
- [Multi-Fault Diagnosis Of Industrial Rotating Machines Using Data-Driven Approach: A Review Of Two Decades Of Research](https://mlsanity.com/paper/2206.14153/) (2022)
- [Explainable AI Algorithms for Vibration Data-based Fault Detection: Use Case-adadpted Methods and Critical Evaluation](https://mlsanity.com/paper/2207.10732/) (2022)
- [Detecting train driveshaft damages using accelerometer signals and Differential Convolutional Neural Networks](https://mlsanity.com/paper/2211.09011/) (2022)
- [Coalitional Bayesian Autoencoders -- Towards explainable unsupervised deep learning](https://mlsanity.com/paper/2110.10038/) (2021)

## Abstract (arXiv)

In this survey paper, we systematically summarize existing literature on bearing fault diagnostics with machine learning (ML) and data mining techniques. While conventional ML methods, including artificial neural network (ANN), principal component analysis (PCA), support vector machines (SVM), etc., have been successfully applied to the detection and categorization of bearing faults for decades, recent developments in deep learning (DL) algorithms in the last five years have sparked renewed interest in both industry and academia for intelligent machine health monitoring. In this paper, we first provide a brief review of conventional ML methods, before taking a deep dive into the state-of-the-art DL algorithms for bearing fault applications. Specifically, the superiority of DL based methods over conventional ML methods are analyzed in terms of fault feature extraction and classification performances; many new functionalities enabled by DL techniques are also summarized. In addition, to obtain a more intuitive insight, a comparative study is conducted on the classification accuracy of different algorithms utilizing the open-source Case Western Reserve University (CWRU) bearing dataset. Finally, to facilitate the transition on applying various DL algorithms to bearing fault diagnostics, detailed recommendations and suggestions are provided for specific application conditions such as the setup environment, the data size, and the number of sensors and sensor types. Future research directions to further enhance the performance of DL algorithms on health monitoring are also discussed.