Rolling bearing intelligent faults diagnosis based on deep convolutional neural network

Wenjie Wang; Rongxin Lv; Haonan Ding; Tengfei Li; Tingyu Li; Xin Wang

doi:10.1117/12.2674425

28 April 2023 Rolling bearing intelligent faults diagnosis based on deep convolutional neural network

Wenjie Wang, Rongxin Lv, Haonan Ding, Tengfei Li, Tingyu Li, Xin Wang

Proceedings Volume 12626, International Conference on Signal Processing, Computer Networks, and Communications (SPCNC 2022); 1262615 (2023) https://doi.org/10.1117/12.2674425
Event: International Conference on Signal Processing, Computer Networks, and Communications (SPCNC 2022), 2022, Zhuhai, China

Abstract

In the processing, manufacturing, and production of modern fields, rolling bearings, the most basic module of most mechanical equipment, have a key role that cannot be ignored. This paper proposes three fault diagnosis model architectures for rolling bearings based on the deep convolutional neural network. Three models were tested on the industry-common Case Western Reserve University Dataset (CWRU). The original vibration signal acquisition and processing module mainly uses the vibration signal window translation method to complete the segmentation of overlapping signals and uses the Inception network to efficiently complete one-dimensional signal preprocessing. Finally, we use t-distributed stochastic neighbor embedding (t-SNE) to reduce the dimension and visualize the learned fault data distribution.

Citation Download Citation

Wenjie Wang, Rongxin Lv, Haonan Ding, Tengfei Li, Tingyu Li, and Xin Wang "Rolling bearing intelligent faults diagnosis based on deep convolutional neural network", Proc. SPIE 12626, International Conference on Signal Processing, Computer Networks, and Communications (SPCNC 2022), 1262615 (28 April 2023); https://doi.org/10.1117/12.2674425

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KEYWORDS

Data modeling

Deep convolutional neural networks

Signal processing

Feature extraction

Convolution

Education and training

Visualization

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