Two-Distribution Approximate Evaluation of Multiwavelet Cascade Feature Method for Noise Unbalanced Bearing Fault Diagnosis

The health status of mechanical equipment bearings is highly variable under complex environment, with fault progression often accompanied by substantial noise interference. Additionally, the timing of fault occurrence is unpredictable, significantly compromising the accuracy of fault diagnosis. Therefore, this article proposes a two-distribution approximate evaluation multiwavelet cascade feature method for addressing the problem of noise-unbalanced bearing fault diagnosis, and multiple wavelet filters to adapt to the filtering requirements of various faults are introduced in this method. Additionally, network branches are cascaded to enhance the characterization capability of network. The two-distribution approximate evaluation method is employed to assign differential weights to the cascaded network features, thereby achieving high-accuracy fault diagnosis. Specifically, measuring the feature approximation degree of the branch cascade and dynamically selecting the best result of the cascade before the decision level is conducive to accurately locating the differential expression of the multiwavelet kernel function, which expands the expected feature information during the network training process and can effectively improve the fault diagnosis performance of the network. The experimental platform signal is used for performance testing and fault diagnosis of rotary bearing in real operation, which can accurately identify the signals of mechanical equipment loading, normal operation, performance decline, and other stages. The experimental diagnostic results from both the public datasets and the slewing bearing working datasets concurrently validate the reliability of the diagnostic method.