Abstract:Aiming at the problems of strong noise interference, high subjective influence of artificial identification characteristics and low accuracy of automatic identification in diesel engine fault diagnosis,a diesel engine fault diagnosis method using convolutional neural network to identify the cumulant grayscale image of vibration signal was proposed. Firstly, using the innate characteristics of third-order cumulants to suppress Gaussian noise, the vibration signal of cylinder head was analyzed, and the grayscale image was generated as input of convolution neural network. Stochastic gradient descent with momentum algorithm and learning rate annealing method were used to train the convolutional neural network, and genetic algorithm was used to optimize training parameters, then the trained network was used to diagnose the faults of five working conditions of diesel engine high pressure oil circuit. The experimental results show that the grayscale images generated by third-order cumulants can suppress the noise effectively and display the characteristic information comprehensively. The method of learning rate annealing and genetic algorithm to improve the optimized convolutional neural network has good generalization ability, and has higher accuracy and anti-noise ability than the traditional method.
常春,梅检民,赵慧敏,沈 虹,王双朋. 基于CUM3-CNN的柴油机高压油路故障诊断[J]. 振动与冲击, 2023, 42(3): 174-180.
CHANG Chun, MEI Jianmin, ZHAO Huimin, SHEN Hong, WANG Shuangpeng. Fault diagnosis of diesel engine high pressure oil circuit based on CUM3-CNN. JOURNAL OF VIBRATION AND SHOCK, 2023, 42(3): 174-180.
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