Elevator Running Fault Monitoring Method Based on Vibration Signal

Abstract

According to the one-dimensional characteristics of the vibration signal, this paper proposes an elevator operation fault monitoring method based on one-dimensional convolutional neural network (1-DCNN). It can solve the problems of traditional elevator fault monitoring methods that require complex feature extraction processes and a large amount of diagnostic experience. Because the elevator fault monitoring field has less fault information, it is different from the large sample situation in the field of face recognition. Aiming at the problem of small samples, this paper first preprocesses elevator vibration signals through singular value decomposition (SVD) and wavelet transform, then uses wavelet transform to extract wavelet energy features of the original vibration signals, and then use PCA to reduce the feature data to the dimension with a cumulative contribution rate of greater than 85%. When reducing the dimensionality, the original characteristics of the features are preserved as much as possible. When designing the 1-CNN, the K-fold cross-validation method is added to obtain as many abnormalities from the sample set as possible. The information is finally trained using the 1-CNN and classified by softmax regression. In order to verify the performance of the algorithm, the original vibration signal was used as the input of the 1-CNN, and the wavelet energy feature without PCA dimensionality reduction was used as the input of the 1-CNN. The experimental results showed that the 1-DCNN model with PCA dimension-reduced feature data as input can effectively extract and identify the features of normal and abnormal states and has high fault identification accuracy, and good results have been obtained.