Title: Bearing fault prediction fusion algorithm based on multi-dimensional signal

Authors: Lida Liu; Qimiao Wang; Mei Sun; Yingjie Chen; Peiguang Lin

Addresses: Tsinghua Shenzhen International Graduate School, Shenzhen, 518055, China; Shandong Runyi Intelligent Technology Co. Ltd., Jinan, 250000, China ' School of Computing and Artificial Intelligence, Shandong University of Finance and Economics, Jinan, 250014, China ' School of Finance and Taxation, Shandong University of Finance and Economics, Jinan, 250014, China ' School of Computing and Artificial Intelligence, Shandong University of Finance and Economics, Jinan, 250014, China ' School of Computing and Artificial Intelligence, Shandong University of Finance and Economics, Jinan, 250014, China

Abstract: In practical industrial applications of fault prediction, due to the complex noise environment, the effective characteristics of extraction from the collected bearing vibration signal are very difficult and there is a problem of data breach. The prediction accuracy and adaptability of traditional models are insufficient. Therefore, this paper proposes a multi-dimensional signal-based CNN integration learning model. By optimising the pre-processing process, this model introduces methods including multi-label data balance and time-frequency domain conversion; Build the optimal framework of the base mode use the CNN and the residual network integration learning algorithm fusion model to improve the accuracy and stability of the prediction. The experimental results show that the percentage of the model error is 20.52%, and as a result, DAF-LSTM (improved deep forest length memory neural network), CNN-LSTM (Space convolution long short-term memory neural network), ResNet-LSTM (long-time residual networks) were reduced by 4.66%, 7.72%, and 2.24%, respectively. Excellent performance can effectively improve the fault prediction capabilities of the bearings and ensure the safe and stable operation of industrial production.

Keywords: fault prediction; deep learning; frequency transformation; CNN; convolutional neural network; residual connection; integrated network.

DOI: 10.1504/IJCSM.2025.147114

International Journal of Computing Science and Mathematics, 2025 Vol.21 No.2, pp.162 - 177

Received: 03 Dec 2024
Accepted: 28 Mar 2025
Published online: 10 Jul 2025 *

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