Non-linear time-varying process fault monitoring method based on high efficiency recursion kernel principal component analysis

Abstract

The invention discloses a non-linear time-varying process fault monitoring method based on high efficiency recursion kernel principal component analysis and belongs to the fault detection and diagnosis technology field. The method comprises steps that data having non-linear and slow time-varying characteristics and containing faults is acquired from a Tennessee Eastman process simulator, a Gauss kernel function is utilized to project the acquired normal data to the high-dimensional characteristic space and is centralized, an initial offline monitoring model is established, and a kernel densityestimation function is employed to determine control limit; secondly, when new process data is acquired, through introducing a first-order interference theory method, a model is directly updated based on a characteristic value and a characteristic vector acquired in the offline model, the new data is projected to the updated kernel space and the residual error space to calculate T2 and SPE statistics; when the corresponding control limit is surpassed, occurrence of a monitoring fault is determined, otherwise, the whole process operates normally. The method is advantaged in that two problems are mainly solved, 1), a problem of relatively high false alarm rate generated during fault monitoring in the non-linear time-varying process of kernel principal component analysis is solved; and 2), aproblem of relatively high load existing in a recursion algorithm based on characteristic constant decomposition is solved.

Description

technical field [0001] The invention belongs to the technical field of fault monitoring and diagnosis, and proposes a non-linear time-varying process fault monitoring method based on efficient recursive kernel principal component analysis. Background technique [0002] With the increasing complexity of industrial process, how to improve the safety and reliability of industrial process system, prevent and avoid the occurrence of process failure has become an urgent problem to be solved. Process monitoring is a technology developed to solve this kind of problems. [0003] For process monitoring and fault diagnosis, traditional multivariate statistical methods represented by Principal Component Analysis (PCA) and Partial Least Squares (PLS) have been successfully applied in the field of industrial process monitoring. Traditional multivariate statistical methods all assume that the process data comes from a single working condition and obeys Gaussian distribution, and the relat...

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Problems solved by technology

For the problem that the data does not conform to the Gaussian distribution, the kernel density estimation method is used to determine the control limit of the statistics, which reduces the false alarm rate of failure

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