Fault diagnosis study of elevator based on stochastic configuration networks

Abstract

Elevators play a vital role in people's daily activities as a vehicle. Once the elevator runs in the process, failure will seriously threaten the user's life and property safety, so the corresponding fault diagnosis of the elevator is necessary for the elevator maintenance process. In this paper, the wavelet soft threshold denoising method is used to reduce the influence of external interference on the diagnosis results, and the time domain features of signals are extracted to form the feature vector. The stochastic configuration network is used to classify the feature vector and establish the elevator fault diagnosis model. Finally, the feasibility of the method is verified by experimental comparison. The final experiment shows that this method has good stability and a high fault recognition rate, which is very important for elevator maintenance.