Industrial fault diagnosis method based on improved KPCA (kernel principal component analysis) and hidden Markov model

Abstract

The invention discloses an industrial fault diagnosis method based on an improved KPCA (kernel principal component analysis) and hidden Markov model and belongs to the technical field of industrial process monitoring and diagnosing. Calculation efficiency of KPCA under the condition of large samples is greatly improved by a similarity analysis method, and industrial faults can be classified by means of high dynamic process time sequence modeling capability and time sequence model classifying capability of the hidden Markov model. Accordingly, compared with the existing methods of the prior art, the industrial fault diagnosis method has the advantages that complexity in calculation can be reduced, nonlinear characteristics can be more efficiently processed and nonlinear industrial fault diagnosis is high in accuracy since the nonlinear characteristics and massive data of industrial data are sufficiently considered.

Description

technical field

[0001] The invention belongs to the field of industrial process monitoring and fault diagnosis, in particular to an industrial fault diagnosis method based on improved KPCA and hidden Markov model. Background technique

[0002] As the complexity of industrial processes grows, the effectiveness of industrial process monitoring and diagnostics becomes increasingly important to ensure process safety, maintain product quality, and optimize product profitability.

[0003] For process monitoring and fault diagnosis, traditional methods mostly use multivariable statistical process monitoring (Multivariable Statistical Process Monitoring, MSPM), in which principal component analysis (Principal Component Analysis, PCA), partial least squares (Partial Least Squares, PLS) ) and Independent Component Analysis (ICA) have been successfully applied in industrial process monitoring. Traditional PCA, ICA and other methods all assume that the relationship between process vari...

Examples

Embodiment

[0101] As one of the most important basic industries in the national economy, iron and steel smelting is an important indicator to measure a country's economic level and comprehensive national strength. Blast furnace ironmaking is the most important link in the production process of the iron and steel industry, so it is of great significance to study the abnormal working condition diagnosis and safe operation methods of large blast furnaces.

[0102] The blast furnace is a huge airtight reaction vessel, and its internal smelting process is a typical "black box" operation through a series of complex physical, chemical and heat transfer reactions under high temperature and high pressure conditions. It is precisely because of the complexity inside the blast furnace that its monitoring process has the characteristics of nonlinear, non-Gaussian and multi-modal. Therefore, our proposed method is adaptable to blast furnace fault monitoring. The effectiveness of the method of the pre...

technical field

[0001] The invention belongs to the field of industrial process monitoring and fault diagnosis, in particular to an industrial fault diagnosis method based on improved KPCA and hidden Markov model. Background technique

[0002] As the complexity of industrial processes grows, the effectiveness of industrial process monitoring and diagnostics becomes increasingly important to ensure process safety, maintain product quality, and optimize product profitability.

[0003] For process monitoring and fault diagnosis, traditional methods mostly use multivariable statistical process monitoring (Multivariable Statistical Process Monitoring, MSPM), in which principal component analysis (Principal Component Analysis, PCA), partial least squares (Partial Least Squares, PLS) ) and Independent Component Analysis (ICA) have been successfully applied in industrial process monitoring. Traditional PCA, ICA and other methods all assume that the relationship between process vari...

Examples

Embodiment

[0101] As one of the most important basic industries in the national economy, iron and steel smelting is an important indicator to measure a country's economic level and comprehensive national strength. Blast furnace ironmaking is the most important link in the production process of the iron and steel industry, so it is of great significance to study the abnormal working condition diagnosis and safe operation methods of large blast furnaces.

[0102] The blast furnace is a huge airtight reaction vessel, and its internal smelting process is a typical "black box" operation through a series of complex physical, chemical and heat transfer reactions under high temperature and high pressure conditions. It is precisely because of the complexity inside the blast furnace that its monitoring process has the characteristics of nonlinear, non-Gaussian and multi-modal. Therefore, our proposed method is adaptable to blast furnace fault monitoring. The effectiveness of the method of the pre...

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