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    • Partial Misfire Fault Diagnosis of An Engine Based on Noise Reduction–Residual Neural Network
    DOI：10.13949/j.cnki.nrjgc.2023.03.011
    Key Words:partial misfire  fault diagnosis  vibration signal  wavelet threshold noise reduction  residual neural network  short residual blocks
    Author NameAffiliationE-mail
    PANG Haoqian* State Key Laboratory of Engines， Tianjin University， Tianjin 300354， China panghq@tju.edu.cn 
    ZHANG Pan State Key Laboratory of Engines， Tianjin University， Tianjin 300354， China  
    WANG Wen SINOTRUCK Qingdao Heavy Industry Co.， Ltd.， Qingdao 266111， China  
    WANG Yanjun State Key Laboratory of Engines， Tianjin University， Tianjin 300354， China  
    ZOU Jiahua State Key Laboratory of Engines， Tianjin University， Tianjin 300354， China  
    GAO Wenzhi* State Key Laboratory of Engines， Tianjin University， Tianjin 300354， China gaowenzhi@tju.edu.cn 
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    Abstract:A fault diagnosis method of “noise reduction–residual neural network” based on wavelet threshold denoising and residual neural network was proposed for partial misfire fault diagnosis of engine cylinders. Combining the noise reduction and deep learning algorithm， the signal was denoised by wavelet threshold and fed into the residual neural network for fault diagnosis. Unlike the previous residual network， the short residual block would be utilized to further prevent network degradation. Besides， the large convolution kernel was also used to expand the convolution field of long data input and improve the ability to extract fault characteristics. Experimental results show that this method can not only achieve more than 97% fault diagnosis accuracy for the operation conditions without training， but also achieve high diagnosis accuracy for the noisy signals with Gaussian noise. The performance of the proposed method is proved to be more superiority and excellent than those of other misfire fault diagnosis algorithms.
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