A multi-scale convolutional neural network for bearing compound fault diagnosis under various noise conditions  

作　　者：JIN YanRui QIN ChengJin ZHANG ZhiNan TAO JianFeng LIU ChengLiang 

机构地区：[1]School of Mechanical Engineering,Shanghai Jiao TongUniversity,Shanghai 200240,China

出　　处：《Science China(Technological Sciences)》2022年第11期2551-2563,共13页中国科学（技术科学英文版）

基　　金：supported by the National Key R&D Program of China(Grant No.2020YFB1709604);the State Key Laboratory of Mechanical System and Vibration(Grant No.MSVZD202103);the Shanghai Municipal Science and Technology Major Project(Grant No.2021SHZDZX0102);。

摘　　要：Recently,with the urgent demand for data-driven approaches in practical industrial scenarios,the deep learning diagnosis model in noise environments has attracted increasing attention.However,the existing research has two limitations:(1)the complex and changeable environmental noise,which cannot ensure the high-performance diagnosis of the model in different noise domains and(2)the possibility of multiple faults occurring simultaneously,which brings challenges to the model diagnosis.This paper presents a novel anti-noise multi-scale convolutional neural network(AM-CNN)for solving the issue of compound fault diagnosis under different intensity noises.First,we propose a residual pre-processing block according to the principle of noise superposition to process the input information and present the residual loss to construct a new loss function.Additionally,considering the strong coupling of input information,we design a multi-scale convolution block to realize multi-scale feature extraction for enhancing the proposed model’s robustness and effectiveness.Finally,a multi-label classifier is utilized to simultaneously distinguish multiple bearing faults.The proposed AM-CNN is verified under our collected compound fault dataset.On average,AM-CNN improves 39.93%accuracy and 25.84%F1-macro under the no-noise working condition and 45.67%accuracy and 27.72%F1-macro under different intensity noise working conditions compared with the existing methods.Furthermore,the experimental results show that AM-CNN can achieve good cross-domain performance with 100%accuracy and 100%F1-macro.Thus,AM-CNN has the potential to be an accurate and stable fault diagnosis tool.

关 键 词：ANTI-NOISE residual pre-processing block bearing compound fault multi-label classifier multi-scale convolution feature extraction 

分 类 号：TH133.3[机械工程—机械制造及自动化] TP183[自动化与计算机技术—控制理论与控制工程]