Non-linear procedure fault identification method based on kernel principal component analysis contribution plot

Abstract

The invention relates to a non-linear fault identifying method based on a PCA (principal component analysis) contribution diagram, and includes five steps, namely, collecting data, extracting the base of the feature space, extracting the non-linear principal component, detecting faults and identifying faults. The invention puts forward a new method for extracting the base of the feature space, reduces the linear redundancy after the data are mapped to the feature space by extracting the base and decreases the calculation amount of KPCA when training samples are large in quantity. The invention adopts the contribution diagram to identify faults, calculates out the contribution diagram and the control limit for each variant in the process and determines the responsibility for the occurrenceof faults that each variant should shoulder when each variant is out of control through the relationship between the contribution diagram and the control limit. The identifying method overcomes the trouble in identifying faults due to the fact that the input space and the feature space cannot be converted freely.

Description

Technical field [0001] The invention belongs to the technical field of fault detection and diagnosis, and proposes a nonlinear process fault identification method based on the contribution graph of nuclear principal component analysis. Background technique [0002] In the past ten years, multivariate statistical methods have been rapidly developed in process monitoring including fault detection and fault diagnosis. Such as principal component analysis (PCA), partial least squares (PLS), independent component analysis (ICA) and support vector basis (SVM) have been widely used in industrial processes. However, these methods mainly solve the problem of linear change. Process monitoring based on these methods generally assumes that the process is linear, and actual industrial processes have different degrees of nonlinear changes. For processes with strong nonlinear changes, applying these methods for online monitoring will produce a higher false alarm rate. In order to solve the no...

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Problems solved by technology

For KPCA, since the nonlinear mapping function from the input space to the feature space is unknown, the contribution map similar to PCA cannot be directly used for the fault variable identification of KPCA

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