Bearings Intelligent Fault Diagnosis by 1-D Adder Neural Networks  

作　　者：Jian Tang Chao Wei Quanchang Li Yinjun Wang Xiaoxi Ding Wenbin Huang 

机构地区：[1]College of Mechanical Engineering,Chongqing University,Chongqing 400044,China [2]Department of Mechanical and Materials Engineering,Queen’s University,Kingston,Canada

出　　处：《Journal of Dynamics, Monitoring and Diagnostics》2022年第3期160-168,共9页动力学、监测与诊断学报（英文）

基　　金：support provided by the China National Key Research and Development Program of China under Grant 2019YFB2004300;the National Natural Science Foundation of China under Grant 51975065 and 51805051.

摘　　要：Integrated with sensors,processors,and radio frequency(RF)communication modules,intelligent bearing could achieve the autonomous perception and autonomous decision-making,guarantying the safety and reliability during their use.However,because of the resource limitations of the end device,processors in the intelligent bearing are unable to carry the computational load of deep learning models like convolutional neural network(CNN),which involves a great amount of multiplicative operations.To minimize the computation cost of the conventional CNN,based on the idea of AdderNet,a 1-D adder neural network with a wide first-layer kernel(WAddNN)suitable for bearing fault diagnosis is proposed in this paper.The proposed method uses the l1-norm distance between filters and input features as the output response,thus making the whole network almost free of multiplicative operations.The whole model takes the original signal as the input,uses a wide kernel in the first adder layer to extract features and suppress the high frequency noise,and then uses two layers of small kernels for nonlinear mapping.Through experimental comparison with CNN models of the same structure,WAddNN is able to achieve a similar accuracy as CNN models with significantly reduced computational cost.The proposed model provides a new fault diagnosis method for intelligent bearings with limited resources.

关 键 词：adder neural network convolutional neural network fault diagnosis intelligent bearings l1-norm distance 

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]