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

by   Shen Zhang, et al.

In this survey paper, we systematically summarize the current literature on studies that apply machine learning (ML) and data mining techniques to bearing fault diagnostics. Conventional ML methods, including artificial neural network (ANN), principal component analysis (PCA), support vector machines (SVM), etc., have been successfully applied to detecting and categorizing bearing faults since the last decade, while the application of deep learning (DL) methods has sparked great interest in both the industry and academia in the last five years. In this paper, we will first review the conventional ML methods, before taking a deep dive into the latest developments in DL algorithms for bearing fault applications. Specifically, the superiority of the DL based methods over the conventional ML methods are analyzed in terms of metrics directly related to fault feature extraction and classifier performances; the new functionalities offered by DL techniques that cannot be accomplished before are also summarized. In addition, to obtain a more intuitive insight, a comparative study is performed on the classifier performance and accuracy for a number of papers utilizing the open source Case Western Reserve University (CWRU) bearing data set. Finally, based on the nature of the time-series 1-D data obtained from sensors monitoring the bearing conditions, recommendations and suggestions are provided to applying DL algorithms on bearing fault diagnostics based on specific applications, as well as future research directions to further improve its performance.


page 5

page 14

page 15

page 16

page 17

page 18

page 19

page 20


A Systematic Review of Machine Learning Techniques for Cattle Identification: Datasets, Methods and Future Directions

Increased biosecurity and food safety requirements may increase demand f...

Systematic review of deep learning and machine learning for building energy

The building energy (BE) management has an essential role in urban susta...

Improving Accuracy and Explainability of Online Handwriting Recognition

Handwriting recognition technology allows recognizing a written text fro...

A Survey of Predictive Maintenance: Systems, Purposes and Approaches

This paper provides a comprehensive literature review on Predictive Main...

Image Embedding of PMU Data for Deep Learning towards Transient Disturbance Classification

This paper presents a study on power grid disturbance classification by ...

Differential Machine Learning

Differential machine learning (ML) extends supervised learning, with mod...

Label-Efficient Learning in Agriculture: A Comprehensive Review

The past decade has witnessed many great successes of machine learning (...

Please sign up or login with your details

Forgot password? Click here to reset