Fault Diagnosis Method of Diesel Engine Fuel System Based on 1DCNN-GWO-SVM
Accurate and effective fault diagnosis is an important guarantee for the safe and reliable operation of diesel engine. A diesel engine fault diagnosis method based on 1DCNN-GWO-SVM was proposed to address the problems of multiple meas-urement points and strong profession in thermal parameter diagnosis methods,as well as the high human influencing factors and high uncertainty in traditional machine learning combined with vibration signal diagnosis methods.A one-dimensional conv-olutional neural network(1DCNN)was used to extract self-learning features of diesel engine vibration acceleration signals in the time domain.Then the extracted feature vectors were used to train the support vector machine(SVM)classification model.The grey wolf optimization algorithm(GWO)was used to optimize the hyperparameters of SVM such as C and g in order to achieve end-to-end fault diagnosis of diesel engine.For the sample verification,1DCNN-GWO-SVM could achieve a diagnostic accuracy of 99.10％ on the training set,which was superior to various traditional machine learning fault diagnosis methods.Moreover,it could still maintain a diagnostic accuracy of over 90％ in interference environments with signal-to-noise ratios of 10 dB,20 dB, and 30 dB,respectively.The results indicate that 1DCNN-GWO-SVM is an end-to-end fault diagnosis method for diesel engine fuel injection systems with high prediction accuracy,strong generalization ability,and strong anti-interference ability,which has practical engineering application value.
convolutional neural networksupport vector machinegrey wolf optimization algorithmdiesel enginefault diagnosis
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