Load identification method of track driving system based on genetic neural network

Here, aiming at practical engineering problems of bad working environment of track driving system of coal mine robot and load being unable to obtain directly and effectively, a vibration signal load identification method based on genetic neural network was proposed. The load identification model based on genetic algorithm (GA)-optimized and back propagation (BP) neural network was constructed. 5 sets of vibration acceleration data and a set of stress load data of a track car were collected with the road test method. Effects of road roughness frequency and driving wheel meshing frequency on vibration and stress load of the track car were discussed. The original stress load data was denoised by means of fast Fourier transform (FFT). According to the ride comfort index of track car, Sym8 wavelet function was used to do 5 layer feature extraction for vibration acceleration signals, and improve the accuracy of load identification. Then, 5 sets of vibration acceleration data after decomposed using wavelet transform and filtered stress load data were taken as the input and output of the GA-BP neural network to perform training and verification to reveal the relationship between vibration and stress load in motion of track driving system. The results showed that the road roughness frequency, driving wheel meshing frequency and rotating frequency are main frequency components of the track car's vibration; the vibration frequency caused by road roughness is 13.765 Hz, the driving wheel meshing frequency is 68.25 Hz and the rotating frequency is 3.25 Hz; the optimal hidden layer neuron number of BP neural network obtained from multi-set tests is 63; the stress load identified with the GA-BP neural network has a higher degree of consistence to the expected stress load, and the relative error is 4.5% to verify the effectiveness of the proposed method and provide a good theoretical basis for studying reliability of track driving system in coal mine machinery. © 2022, Editorial Office of Journal of Vibration and Shock. All right reserved.