Traction Machine State Recognition Method Based on DPCA Algorithm and Convolution Neural Network

It is important to improve the identification accuracy of the operating status of elevator traction machines. The distribution difference of the time-frequency signals utilized to identify operating circumstances is modest, making it difficult to extract features from the vibration signals of traction machines under various operating conditions, leading to low recognition accuracy. A novel method for identifying the operating status of traction machines based on signal demodulation method and convolutional neural network (CNN) is proposed. The original vibration time-frequency signals are demodulated by the demodulation method based on time-frequency analysis and principal component analysis (DPCA). Firstly, the signal demodulation method based on principal component analysis is used to extract the modulation features of the experimentally measured vibration signals. Then, The CNN is used for feature vector extraction, and the training model is obtained through multiple iterations to achieve automatic recognition of the running state. The experimental results show that the proposed method can effectively extract feature parameters under different states. The diagnostic accuracy is up to 96.94%, which is about 16.61% higher than conventional methods. It provides a feasible solution for identifying the operating status of elevator traction machines.