Bearing Fault Diagnosis Method Based on RCMFDE-SPLR and Ocean Predator Algorithm Optimizing Support Vector Machine

Abstract

For the problem that rolling bearing fault characteristics are difficult to extract accurately and the fault diagnosis accuracy is not high, an unsupervised characteristic selection method of refined composite multiscale fluctuation-based dispersion entropy (RCMFDE) combined with self-paced learning and low-redundant regularization (SPLR) is proposed, for which the fault diagnosis is carried out by support vector machine (SVM) optimized by the marine predator algorithm (MPA). First, we extract the entropy characteristics of the bearings under different fault states by RCMFDE and the introduction of the fine composite multiscale coarse-grained method and fluctuation strategy improves the stability and estimation accuracy of the bearing characteristics; then, a novel dimensionality-reduction method, SPLR, is used to select better entropy characteristics, and the local flow structure of the fault characteristics is preserved and the redundancy is constrained by two regularization terms; finally, using the MPA-optimized SVM classifier by combining Levy motion and Eddy motion strategies, the preferred RCMFDE is fed into the MPA–SVM model for fault diagnosis, for which the obtained bearing fault diagnosis accuracy is 97.67%. The results show that the RCMFDE can effectively improve the stability and accuracy of the bearing characteristics, the SPLR-based low-dimensional characteristics can suppress the redundancy characteristics and improve the effectiveness of the characteristics, and the MPA-based adaptive SVM model solves the parameter randomness problem and, therefore, the proposed method has outstanding superiority.

A Multiscale Recursive Attention Gate Federation Method for Multiple Working Conditions Fault Diagnosis

“…When the data quality is better, the spectrum analysis can find the characteristic frequency of the fault and realize fault diagnosis. However, when the data quality is poor, the feature frequency of the fault is difficult to present on the spectrum [ 4 , 5 ].…”

Relative Density-Based Intuitionistic Fuzzy SVM for Class Imbalance Learning

“…The class imbalance learning problem in binary classification occurs when the number of one category is significantly greater than that of the other category [1,2]. The imbalance datasets exist in various application domains, such as biological recognition [3,4], medical diagnosis [5,6], fault diagnosis [7], credit card fraud detection [8,9], and text categorization [10,11], etc. When tackling imbalanced datasets, due to the main role of the majority class, the traditional classification methods designed for balanced datasets may not always achieve good classification performance for the minority class.…”