Rolling bearing fault diagnosis based on data-level and feature-level information fusion

Abstract

To address the limitation of single acceleration sensor signals in effectively reflecting the health status of rolling bearings, a rolling bearing fault diagnosis method based on the fusion of data-level and feature-level information was proposed. First, according to the impact characteristics of rolling bearing faults, correlation kurtosis rules were designed to guide the weight distribution of multi-sensor signals. These rules were then combined with a weighted fusion method to obtain high-quality data-level fusion signals. Subsequently, a feature-fusion convolutional neural network(FFCNN)that merges the one-dimensional(1D)features extracted from the fused signal with the two-dimensional(2D)features extracted from the wavelet time-frequency spectrum was designed to obtain a comprehensive representation of the health status of rolling bearings. Finally, the fused features were fed into a Softmax classifier to complete the fault diagnosis. The results show that the proposed method exhibits an average test accuracy of over 99.00% on the two rolling bearing fault datasets, outperforming other comparison methods. Thus, the method can be effectively utilized for diagnosing rolling bearing faults.