Elevator operation abnormality detection method based on spare denoising self-coding

Abstract

The invention discloses an elevator operation abnormity detection method based on sparse denoising self-coding. The method comprises the following steps: correspondingly acquiring time domain waveforms of normal vibration and abnormal vibration of an elevator; obtaining frequency domain waveforms of the normal vibration and the abnormal vibration according to the time domain waveforms of the normal vibration and the abnormal vibration; manufacturing a training set and a test set according to the time domain waveform and the frequency domain waveform; learning a training set by adopting single-layer sparse denoising self-coding to obtain a first neural network; adjusting the first neural network by adopting a stacked sparse denoising self-coding and BP algorithm to obtain a second neural network; testing the second neural network by using a test set to obtain a time domain reconstruction error and a frequency domain reconstruction error of each sample so as to obtain a fusion reconstruction error sequence; setting the median value of the fusion reconstruction error sequence as a threshold value for distinguishing normal data from abnormal data; and judging whether the signal to be detected is abnormal or not by utilizing a threshold value and a second neural network. The method solves the problem of too few abnormal samples and improves the efficiency and accuracy of abnormal detection.

Description

technical field [0001] The invention relates to the technical field of anomaly detection, in particular to an elevator operation anomaly detection method based on sparse denoising self-encoding. Background technique [0002] Anomaly detection is a very important problem in many fields. The goal of an anomaly detection task is to identify models that are inconsistent with expectations, and such inconsistent models are defined as outliers. Among the anomaly detection technologies that have been researched and developed, they involve credit card fraud detection, network intrusion detection, disease diagnosis detection, bearing fault detection, etc. [0003] The spectral anomaly detection task in elevator quality assessment is very different from other anomaly detection tasks, mainly in two aspects: (1) The diversity of anomaly types makes it impossible to label anomaly data. (2) The complexity and number of elevator working conditions increase the difficulty of manual feature...

AI Technical Summary

Problems solved by technology

[0005] The purpose of the present invention is to provide a method for detecting abnormal elevator operation based on sparse denoising self-encoding, which can solve the problems of diversity of abnormal types, uneven distribution of data, and large data complexity and quantity during elevator operation, thereby Realize abnormal detection of elevator operation

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