Elevator failure recognition method based on convolutional neural network

Abstract

The invention discloses an elevator failure recognition method based on a convolutional neural network. The elevator failure recognition method comprises the steps that 1, elevator motion data are collected and are converted into a time-spectrogram as a sample set through wavelet transform; 2, the time-spectrogram in the sample set is divided into a training set and a test set, failure types and failure degrees of samples in the training set are marked as known labels of data samples; 3, the convolutional neural network is built, the time-spectrogram in the training set is input to the convolutional neural network, and the characteristics of the previous layer are extracted and classified; 4, according to the labels given in the step 2 and the characteristics extracted in the step 3, a multi-class SVM classifier is trained; 5, after training, the prediction rates, for all types of failures, obtained through the SVM classifier are obtained; and 6, detection and recognition are conducted. The elevator failure recognition method which is novel in angle, consistent to actual situation and high in accuracy is achieved, the requirement for hardware is low, and portability is high.

Description

technical field [0001] The invention belongs to the field of elevator fault detection, in particular to an elevator fault identification method based on a convolutional neural network. Background technique [0002] For the current situation of the domestic elevator industry, there are still many problems in the monitoring of the movement state of the elevator car. Due to the large base of elevators in my country, although the elevator industry is developing steadily, the level of elevator repair and maintenance closely related to it is difficult to keep up with the pace of the industry, resulting in a high rate of elevator safety accidents in my country. [0003] At present, most building elevators work in an independent and closed environment, and maintenance personnel cannot grasp the working status of the elevator in real time and deal with problems arising during elevator operation in a timely manner. Moreover, the country lacks efficient and accurate monitoring technic...

AI Technical Summary

Problems solved by technology

[0005] 1) Most of them use the data cabinet in the elevator motor system as the data source, so it depends on the specific elevator type and model, and the portability is poor;

[0006] 2) The fault judgment method is to simply check whether the elevator motion data exceeds a fixed threshold, and the threshold cannot be adjusted according to the different motion states of the elevator, so it cannot be judged in combination with the motion state, and the accuracy is poor;

[0007] 3) The fault judgment method is based on the data analysis of the signal in the time domain, without considering the information provided by the signal in the frequency domain, so the source of information is single and the accuracy is poor

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