Industrial process fault diagnosis method based on KPCA

Abstract

The invention discloses an industrial process fault diagnosis method based on KPCA. The industrial process fault diagnosis method based on KPCA comprises steps of extracting a principal element sub space load direction and a residual sub space load direction through the KPCA method from historically-normal data during the industrial production procedure, extracting the principal element sub space load direction and the residual sub space load direction through the KPCA method from historically-faulted data with known faults, performing T2-based statistical magnitude reconstruction and SPE-based statistical magnitude reconstruction on the historically-faulted data, the fault type of which is known, extracting the fault characteristic direction of the historically-faulted data which is reconstructed specific to the T2 statistical magnitude and extracting the fault characteristic direction of the historically-faulted data which is reconstructed specific to the SPE statistical magnitude, obtaining the collection of the reconstruction fault characteristic direction, collecting industrial production procedure new data in real time, utilizing the KPCA method to calculate the T2 statistical magnitude and the SPE statistical magnitude by, determining whether faults happen during the industrial production procedure, utilizing the reconstruction characteristic direction collection to perform fault direction reconstruction on new data, and determining the fault type of the current industrial production procedure.

Description

technical field [0001] The invention belongs to the field of process control, and in particular relates to a KPCA-based industrial process fault diagnosis method. Background technique [0002] Using the data obtained in the industrial production process to model and detect and diagnose the faults in the production process is a very challenging problem, which has received extensive attention in recent years. Many scholars have studied the detection and diagnosis of faults in the production process by using multivariate statistical methods such as PCA and PLS. These methods are able to extract the latent characteristics of the measurement data, and use statistical principles to define detection statistics and their control limits under normal production conditions based on these characteristics. During on-line monitoring, the corresponding statistics are calculated through the new sampling data, and if the result exceeds the limit and an alarm is issued, it is considered that...

AI Technical Summary

Problems solved by technology

However, the traditional fault reconstruction method is a linear modeling method, which cannot reflect the nonlinear characteristics of the data; and this method only focuses on the internal relationship of the fault data, and cannot effectively distinguish the fault information from the normal information in the data. Fault reconstruction based on the fault feature direction extracted by the method will lead to excessive reconstruction

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