Deep learning-based point-by-point classification fault detection method

Abstract

The invention discloses a deep learning-based point-by-point classification fault detection method (DPCFD), and the method specifically comprises the steps: collecting the data of each channel of an industrial system, and obtaining an original data sequence; inputting the original data sequence into a pre-trained sequence state generator model to generate a real-time state sequence of each channel; splicing the real-time state sequence of each channel and the original data sequence according to the time dimension to obtain a fused data sequence, grouping the channels according to the incidence relation between the channels, and inputting the fused data sequence into a pre-trained fault detection model based on deep learning point-by-point classification according to the channel grouping to obtain a sequence of the fault detection results. According to the method, the DPCFD method is evaluated by using a standard data set Tennessee Eschermann, and experimental results show that the method has the advantages of high detection performance and low detection time delay.

Description

technical field [0001] The present application relates to the technical field of fault detection, in particular to a fault detection method based on deep learning point-by-point classification. Background technique [0002] Fault is an unexpected change of system function, generally defined as at least one variable, parameter or characteristic attribute in the system deviates from the normal range, usually causing performance deterioration or loss of function of components and systems. Finding faults early and quickly is of great significance to the prevention of major accidents. Therefore, important systems are generally monitored in real time by multiple sensor data, and fault detection based on these multivariate real-time monitoring data is very difficult. The main challenges are as follows Several aspects: ①The data of each channel is time series, and their time series relationship is often nonlinear, and the performance of each component will attenuate with use, which ...

AI Technical Summary

Problems solved by technology

When using such classification-based methods for fault detection, it is only possible to determine the approximate time period of the fault, but cannot precisely locate the start and end time of the fault

If the predefined timing segment is too long, it will be more difficult to determine the exact time of the start and end of the fault, or even know whether the fault is over, which is not conducive to reasonable handling of the fault; on the contrary, if the predefined timing segment is too short, it will not be possible Make full use of timing information, the detection effect is difficult to guarantee

Images