Fault Diagnosis Method and Experiment of Cotton Picking Head Based on Particle Swarm Optimization Algorithm and SDAE

In view of lack of fault diagnosis and fault warning of cotton pickers, a stacked denoising autoencoder (SDAE) fault diagnosis method based on particle swarm optimization (PSO) was proposed. The ratio between the speed of drum and speed of hydraulic drive device and the pressure of hydraulic drive device were used as the input of the fault diagnosis model of the cotton picking head. The PSO algorithm was used to self-adapt the number of hidden layer nodes, sparse parameters and the zero setting ratio of the input data in the SDAE network to determine the network structure. Then, the pre-processed data were input into the PSO SDAE network for depth feature extraction. After forward propagation and reverse fine-tuning, the fault diagnosis model of picking head was obtained. Through the simulation test on the blockage fault of the cotton head, signals such as the speed of the drum and the pressure of the hydraulic drive device were obtained, and parameters of the normal operation, slight blockage and severe blockage of the drum of the cotton head were obtained. The original blockage fault data sample of the cotton head was formed, and the fault data sample was input into the fault diagnosis model of the cotton head to verify the algorithm. The test results showed that PSO SDAE network diagnosis method was superior to SDAE network, support vector machines (SVM), back propagation neural network (BPNN) and deep belief network (DBN) in terms of feature extraction and fault diagnosis accuracy. PSO SDAE fault diagnosis model can be used for fault diagnosis and early warning of cotton picker, which can reduce the failure rate of cotton picker and improve the working efficiency. © 2023 Chinese Society of Agricultural Machinery. All rights reserved.