Industrial process fault diagnosis method based on similarility local spline regression

Abstract

The invention provides an industrial process fault diagnosis method based on similarity local spline regression, and relates to the technical field of fault monitoring and diagnosis. The method includes the steps of collecting industrial process data, performing partial labelling and standardization processing, obtaining forecast labels by using an LSR method, processing the forecast labels by using a similarity analysis method, performing label correction on fuzzy fault identification points, then constructing an online diagnosis model based on a spline function, obtaining a coefficient matrix by using a ridge regression method, collecting new data during the industrial production process, obtaining the corresponding labels through the online diagnosis model and the coefficient matrix, and performing fault diagnosis. The industrial process fault diagnosis method in the invention solves the problem in fault diagnosis of multi-source heterogeneous data including a large number of physical and chemical variables, images, audio, video and the like in an industrial production process, without the need for fault identification of all industrial production data, thereby saving a lot of time and manpower, and can greatly reduce false alarms and improve the accuracy and sensitivity of fault detection.

Description

technical field [0001] The invention relates to the technical field of fault monitoring and diagnosis, in particular to an industrial process fault diagnosis method based on similarity local spline regression. Background technique [0002] In the process of industrial production, the fault detection of industrial systems is a critical step, because it is directly related to whether the production can run normally and the quality of the produced products. The purpose of process monitoring is to monitor the operating status of the system. Detect whether a fault occurs in the industrial production process, and quantitatively analyze the abnormal change range of the fault system, judge the fault type, occurrence time, change range and impact degree, and propose corresponding maintenance and improvement measures when necessary, which will greatly reduce the number of enterprises. The danger of the production process is improved, and the safety and security of production are impr...

AI Technical Summary

Problems solved by technology

[0005] The technical problem to be solved in the present invention is to provide an industrial process fault diagnosis method based on similarity local spline regression, referred to as the SLSR method, to solve the problem of a large number of physical and chemical variables and For fault diagnosis of multi-source heterogeneous big data such as images, audio, video, etc., there is no need for fault identification of all industrial production data, which saves a lot of time and manpower, not only greatly reduces false alarms, but also improves the accuracy and sensitivity of fault detection

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