关键词: 松土装置, 工况识别, 算数优化算法, 变分模态分解, 双向长短期记忆网络

Abstract:  When the stress and strain of the loose tooth rake, the key device of the drum film recovery machine, is monitored in real time, the obtained stress and strain signal is easy to be interfered by the external environment and it is difficult to identify the back‑up fault from the signal. In order to solve this problem, the strain monitoring position of the pine tooth harrow was determined by ANSYS analysis, and the strain gauge was used to carry out strain monitoring tests on the pine tooth harrow in different working conditions. Based on the monitoring data, a condition recognition method of variational mode decomposition (VMD)-BiLSTM neural network model based on arithmetic optimization algorithm (AOA) was proposed. Firstly, the parameters of k value and penalty factor α of VMD modal component were optimized by AOA. Then, VMD was used for adaptive decomposition of strain signal of pine tooth harrow. Finally, according to Pearson coefficient, the decomposed and reconstructed signals were input into BiLSTM network for feature learning, so as to realize the condition recognition of the pine tooth rake. The results show that the method can accurately recognize 4 kinds of working conditions such as no‑load, normal working conditions, slight back‑up and severe back‑up, and the effect is better than VMD-LSTM, BiLSTM and LSTM neural network models, with the recognition accuracy of more than 99.1%, which effectively improves the recognition accuracy of working conditions of the pine tooth harrow.

Key words: soil loosening device, condition identification, arithmetic optimization algorithm(AOA), variational mode decomposition (VMD), BiLSTM