基于遗传神经网络的履带行驶系统载荷识别方法

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Abstract In view of the practical engineering problem that the load cannot be obtained directly and effectively due to the adverse working conditions of the crawler travel system of coal mining robot, a method of vibration signal load identification based on genetic neural network is proposed. A genetic algorithm optimized BP(back propagation) neural network load identification model was constructed. The road test method was used to collect 5 sets of vibration acceleration data and a single set of stress load data of the crawler travel system. Discussed the influence of road roughness frequency and driving wheel meshing frequency on the vibration and stress load of crawler travel system. Used fast Fourier transform to denoise the original stress load data. According to the ride comfort index of the crawler travel system, the sym8 wavelet function was used to extract the five-layer feature of the vibration acceleration signal to improve the accuracy of load identification. Then 5 groups of wavelet transform decomposed acceleration data and filtered stress load data were used as the input and output of the GA-BP neural network for training and verification, revealing the relationship between vibration and stress load in the moving process of the crawler travel system. The results show that road roughness frequency, meshing frequency, and rotating frequency are the main frequency components of crawler vibration, the vibration frequency caused by road roughness is 13.765 Hz, the meshing frequency of the driving wheel is 68.25 Hz, and the rotating frequency is 3.25 Hz. After many tests, the best hidden layer neurons number of BP neural network is 63. The stress load identified by the GA-BP neural network is highly consistent with the expected stress load, and the relative error is 4.5%, which verifies the effectiveness of the method. It provides a good theoretical basis for the reliability research of the crawler travel system of coal mine machinery.

Key words： crawler travel system load identification vibration test load GA-BP neural network wavelet transform

Received: 14 October 2020 Published: 15 February 2022

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	ZHANG Zhihong
	ZHANG Hong
	CHEN You
	LI Zhi
	LI Guohua
	FU Zheng

Cite this article:

ZHANG Zhihong,ZHANG Hong,CHEN You, et al. Load identification method of track driving system based on genetic neural network[J]. JOURNAL OF VIBRATION AND SHOCK, 2022, 41(3): 54-61.

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http://jvs.sjtu.edu.cn/EN/ OR http://jvs.sjtu.edu.cn/EN/Y2022/V41/I3/54

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