Fault Diagnosis Method of Industrial Process Based on Direction Kernel Partial Least Squares

Abstract

The invention relates to an industrial process fault diagnosis method based on a direction kernel partial least square. The method is characterized in that historical normal data of an input variable and an output variable of an industrial process is acquired, wherein a fault is easily generated in the industrial process; an operation based on the direction kernel partial least square is performed on the historical normal data; a control limit of Hotelling statistics of the historical normal data and a control limit of a squared prediction error of the historical normal data are calculated; sampling data of the input variable of the industrial process is collected and the operation based on the direction kernel partial least square is performed on the sampling data so as to acquire process monitoring statistics of the sampling data and a squared prediction error of the sampling data are obtained; when the process monitoring statistics control limit of the sampling data or the squared prediction error of the sampling data exceeds the control limit, the sampling data possesses one kind of fault; historical fault data of a known fault type is acquired; reconstruction based on the Hotelling statistics and reconstruction based on the squared prediction error are performed on the historical fault data of the known fault type and a fault type of the sampling data is determined.

Description

technical field [0001] The invention belongs to the technical field of fault monitoring and diagnosis of industrial processes, in particular to an industrial process fault diagnosis method based on direction kernel partial least squares. Background technique [0002] With the continuous development of modern science and technology, especially the rapid improvement of the level of computer science and control, modern industrial processes are becoming more and more large-scale, complex, integrated, and high-speed. While improving the production efficiency and output of the industrial process, how to improve the safety and reliability of the industrial process system, and how to prevent and eliminate the occurrence and development of failures that affect the normal operation of the system has become an important problem to be solved. Process monitoring is a technology developed to solve such problems. On the one hand, its purpose is to deepen the understanding of the system by...

AI Technical Summary

Problems solved by technology

Although the traditional partial least squares (PLS) modeling, monitoring and diagnosis methods are widely used, there are still some problems in the method itself, which hinder the accurate modeling and accurate monitoring of the production process

The first problem is that the residuals of PLS ​​still contain the variation related to the input variables

Due to the existence of output-related variability and not being interpreted, PLS has limitations in monitoring and diagnosing faults related to output variables and cannot achieve the best results

The second problem is that the amount of variation in the PLS residual space is large, making it unsuitable to monitor it with the Squared Prediction Error (SPE) monitoring statistic

PCA is to maintain the most important data distribution direction, which can effectively represent the data distribution characteristics, but the PCA model only studies the internal relationship of the fault data, and cannot effectively isolate the fault information and normal information in the data, and the reconstruction based on PCA Poor adaptability to production processes that focus on product quality

In addition, in the actual industrial process, the variables often show nonlinear characteristics, and the reconstruction using the traditional linear method cannot achieve satisfactory results. Therefore, it is necessary to improve the traditional reconstruction method to improve the level of fault diagnosis.

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