An Empirical Study of Machine Learning and Deep Learning Algorithms on Bearing Fault Diagnosis Benchmarks

Abstract

Rolling element bearings are critical components regarding the reliability and safety of rotating machinery. A reliable and continuous monitoring system with high prediction accuracy prevents machine downtime, increases productivity, and reduces maintenance costs. Vibration analysis via machine learning tools is a well-established approach. In recent years, deep learning methods have received increasing attention from researchers and engineers because of their inherent capability of dealing with big data, mining complex representations, and overcoming the disadvantage of traditional fault classification and feature selection algorithms based on hand-crafted features. However, the literature lacks a well-structured set of rules and comprehensive evaluation of the existing methods and resources and it is not clear how to choose the best algorithm for certain situations to achieve the optimal outcome. This work evaluates traditional machine learning and recent deep learning-based fault classification methods based on two benchmark rolling element bearing datasets and provides a comprehensive evaluation of the methods. Both time-frequency domain statistical features and raw inputs were used. The comparisons were made based on classification accuracy, training time, and hyperparameter tuning, Based on the evaluation results, we discuss technical challenges and provide suggestions for method selection and improvement.