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.

Keywords

fault diagnosis / information fusion / correlation kurtosis / feature-fusion convolutional neural network

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Funding

The National Natural Science Foundation of China(U22A20178); National Key Research and Development Program of China(2022YFB3404800); Jiangsu Province Science and Technology Achievement Transformation Special Fund Program(BA2023019)