Process industry fault detection and prediction method based on PLS analysis

Abstract

The invention provides a process industry fault detection and prediction method based on PLS analysis, and relates to the technical field of fault detection and prediction. The process industry faultdetection and prediction method based on PLS analysis comprises the following steps: S1, determining the number of PLS principal components; S2, obtaining a PLS multivariable statistical process detection graph. and S3, executing a process analysis step based on PLS. According to the PLS analysis-based process industry fault detection and prediction method, the PLS method converts a multiple regression problem into a unary regression problem by utilizing orthogonal projection, the colinearity problem is effectively solved, and the method obtains mutually orthogonal feature vectors of measurement variables by mapping high-dimensional data into low-dimensional data, and then establishes a linear regression relationship between vectors to characterize the process, so that the precision of themodel can be improved, nonlinear action characteristics of independent variables on dependent variables can be extracted from the established model, and the analysis precision is greatly improved.

Description

technical field [0001] The invention relates to the technical field of fault detection and prediction, in particular to a method for fault detection and prediction of process industry based on PLS analysis. Background technique [0002] The results of complex industrial processes are too large, and with the development of complex industrial processes, the corresponding data analysis methods are still relatively backward, resulting in two situations. On the one hand, with the rapid development of advanced sensors and their industrial measurement technologies In the process of continuous industrial production, a large amount of data has been accumulated, and these data inevitably contain undiscovered swimming information, showing the phenomenon of industrial big data; on the other hand, complex industrial production processes have relatively complex and complex Besides, various noises, errors, and missing data are involved in the collection process of the data generated during...

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Problems solved by technology

[0002] The results of complex industrial processes are too large, and with the development of complex industrial processes, the corresponding data analysis methods are still relatively backward, resulting in two situations. On the one hand, with the rapid development of advanced sensors and their industrial measurement technologies In the process of continuous industrial production, a large amount of data has been accumulated, and these data inevitably contain undiscovered swimming information, showing the phenomenon of industrial big data; on the other hand, complex industrial production processes have relatively complex and complex Besides, various noises, errors, and missing data are involved in the collection process of the data generated during operation, which leads to disordered data and even data errors. It is difficult to find a reasonable model that can effectively express the insertion law.

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