Multi-behavior process monitoring method based on pivot analysis and vectorial data description support

A data description, support vector technology, applied in the direction of comprehensive factory control, comprehensive factory control, electrical program control, etc., can solve the problems of false alarms and omissions in the transition part of working conditions, large amount of calculation, decreased sensitivity of process changes, etc. The effect of tightening statistical limits, troubleshooting, and increasing sensitivity

CN101458522AInactive Publication Date: 2009-06-17ZHEJIANG UNIV
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Patent Information

Authority / Receiving Office
CN · China
Current Assignee / Owner
Publication Date
2009-06-17
Estimated Expiration
Not applicable · inactive patent

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Abstract

The invention discloses a multi-operating process monitor method based on principal component analysis and support vectors data. The method establishes a uniform PCA model to various operating mixed data firstly, puts score vectors of principal component space and residual space to high dimension characteristic space. Two new statistics are established in the characteristic space for monitoring the principal component space and residual space. When the process goes wrong, a fault reconstruction method based on SVDD identifies fault. The method establishes two SVDD statistics monitor model to various operating based that the principal analysis method is used for reducing process variable dimension, reduces statistics limit of processing monitor, increases sensitivity of processing monitoring. In addition, the invention provides a fault reconstruction and identifying method aiming at detected process fault which can locate source of fault commendably, is benefit to removing fault as soon as possible, returns process to normal operation.
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Description

technical field

[0001] The invention belongs to the field of flow industry process monitoring and fault diagnosis, in particular to a multi-working-condition process monitoring, fault reconstruction and identification method based on principal component analysis and support vector data description. Background technique

[0002] As a process performance monitoring and fault diagnosis technology based on multivariate statistical projection theory, multivariable statistical process control (MSPC) has received extensive attention from academia and industry. Since the 1990s, the MSPC method represented by principal component analysis (PCA) and partial least squares (PLS) has been successfully applied in industrial process monitoring. However, the traditional MSPC methods assume that the process operates under a single stable condition. In fact, due to the diversification of products and other reasons, most industrial processes do not operate under a single working condition, and...

Examples

Embodiment Construction

[0018] The present invention aims at the problem of multi-working conditions in industrial process monitoring. First, a unified principal component analysis (PCA) statistical monitoring model is established by using all normal working condition data, which is used for information extraction and dimensionality reduction of process data, and the PCA statistical model structured as X = TP T + T ~ P ~ T = TP T + E , where X is the process data matrix, T, P are the principal component score and loading matrices, is the residual score and loading matrix, E is the residual matrix, and the number of principal components of PCA can be selected by cross-validation method or cumulative variance contribution rate (CPV) method. The principal compo...