Application of deep learning in elevator car vibration fault diagnosis

ZHANG Xing-he1, GAO Bing-peng1, CHEN Fei2, NAN Xin-yuan1
（1.College of Electrical Engineering, Xinjiang University, Urumqi 830001, China;
2.Xinjiang Uygur Autonomous Region Inspection Special Equipment, Urumqi 830001, China）

Abstract: Aiming at the problem of insufficient fault diagnosis accuracy during the operation of the elevator system and the new method of elevator car vibration fault diagnosis in the application of convolutional neural networks，the elevator carrier quality tester was used to collect a large number of vibration signals during the operation of the elevator, then the vibration signals was separated and filtered. A continuous wavelet was used to perform timefrequency transformation on the preprocessed signal, and the transformed RGB image was made the inputted of deep learning model. The application of depth in elevator car vibration fault diagnosis was analyzed. The overall plan of elevator car vibration fault diagnosis was designed and the deep learning plan was implemented through PyTorch. The convolutional neural network was applied to the elevator car vibration fault diagnosis. The deep residual neural network classifier was analyzed and trained through supervised learning and was compared with traditional machine learning methods. The results indicate that the accuracy of the deep learning fault diagnosis scheme is 3% higher than that of the traditional machine learning fault diagnosis method. The deep learning solution can effectively diagnose elevator faults and open up a new path for elevator fault diagnosis.
Key words: elevator car vibration; fault diagnosis; convolution neural network(CNN); deep learning; PyTorch; supervised learning