Intelligent multi-component fault diagnosis method driven by vibration-mapped images of mechanical transmission system

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Journal of Vibration and Shock ›› 2025, Vol. 44 ›› Issue (12) : 201-211.

FAULT DIAGNOSIS ANALYSIS

Intelligent multi-component fault diagnosis method driven by vibration-mapped images of mechanical transmission system

FAN Hongwei*1,2,ZHANG Teng1,XU Haowen1,LI Qingshan1,CHEN Buran1,LI Pengfei1

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Abstract

In order to realize the intelligent multi-component fault diagnosis of bearings, rotors and gears in a mechanical transmission system under low speed, weak faults and few measurement points, a fault diagnosis method based on improved vibration-mapped gray texture images (GTIs) and multi-scale lightweight convolutional neural network is proposed. An improved gray texture image with enhanced features is obtained by robust local binary pattern (RLBP) on the traditional gray image. Based on the standard convolutional neural network (CNN), a multi-scale lightweight CNN (MSLCNN) model is constructed by adding batch normalization, multi-scale convolution and simplified full connection layer. The fault diagnosis experiment under 700r/min and a single measurement point is designed, and seven typical faults of bearings, rotors and gears are simulated. The investigation shows that the model parameters of the proposed fault diagnosis method are 0.30M, the floating point operations (FLOPs) are 127.22M, the model size is 1.17MB, and the average diagnostic accuracy is 98.42%. It provides a new feasible path for the data-driven multi-component fault diagnosis of the mechanical transmission system based on the deep learning.

Key words

Transmission system / Fault diagnosis / Gray texture image / Convolutional neural network

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FAN Hongwei1, 2, ZHANG Teng1, XU Haowen1, LI Qingshan1, CHEN Buran1, LI Pengfei1. Intelligent multi-component fault diagnosis method driven by vibration-mapped images of mechanical transmission system[J]. Journal of Vibration and Shock, 2025, 44(12): 201-211

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