Fault diagnosis of rolling bearing based on CEEMDAN combined with improved one-dimensional multi-scale convolutional neural network

Abstract

Abstract: To solve the weak feature extraction of the fault signals in rolling bearing and the low fault identification accuracy relying on expert's experience, a fault Identification method combining complete ensemble empirical mode decomposition with adaptive denoise and improved one-dimensional convolutional neural network was proposed. First, the complete ensemble empirical mode decomposition is used to denoise the bearing signal, followed by signal reconstruction using Person correlation coefficient method on the obtained intrinsic mode functions (IMFs). Next, the first layer of the network adopts a method of combining large-scale kernels and atrous convolutions, the pyramid scene parsing network is introduced and leading to a proposed one-dimensional multi-scale convolutional network for extracting the fault features and the scale of convolution kernel is optimized by PSO algorithm. Finally, the multi-scale feature information is integrated to complete the network learning. And it is input into the Softmax classifier to achieve the fault category. The method is verified by the bearing data set of Western Reserve University and the rolling bearing faults data collected at the simulating faults on HZXT-DS-001 rig. The results show that to compare with the traditional method, the method proposed in this paper has good diagnosis results.

Key words: adaptive noise complete ensemble empirical mode decomposition, 1D convolutional neural network, multiscale feature extraction, feature visualization, fault diagnosis

CLC Number:

TH165
TP18

MA Ning, ZHAO Rong-zhen, ZHENG Yu-qiao. Fault diagnosis of rolling bearing based on CEEMDAN combined with improved one-dimensional multi-scale convolutional neural network[J]. Journal of Lanzhou University of Technology, 2025, 51(1): 45-54.

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