Complex Working Condition Bearing Fault Diagnosis Based on Multi-Feature Fusion and Improved Weighted Balance Distribution Adaptive Approach

In order to improve the accuracy and generalization ability of fault diagnosis for rotating machinery bearings under complex working conditions, a new model based on multi-feature fusion and improved weighted balance distribution adaptation is proposed. Firstly, an optimized variational mode decomposition algorithm is introduced to denoise the fault signal. Secondly, in order to complement fault information from multiple dimensions, thirteen frequency features and four entropy features are extracted. Then, the 17 features are directly concatenated by dimension to form a high-dimensional feature vector that better adapts to complex working conditions and multiple fault modes. Finally, the improved weighted balance distribution adaptive algorithm is used to reduce the distribution difference between the source domain and the target domain. K-nearest neighbors is used as a classifier to determine the fault category. Using the Case Western Reserve University dataset for validation, the experimental results show that the proposed model achieves an average diagnostic accuracy of 99.34% under 12 complex working conditions.