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Method of monitoring faults in sections for intermittent control system

A fault monitoring and control system technology, applied in the direction of electrical testing/monitoring, etc., can solve the problem that batch nonlinear restrictions cannot be removed, and achieve the effect of practical application, strong operability, high sensitivity, and reduced false alarms

Inactive Publication Date: 2013-09-04
SHENYANG INSTITUTE OF CHEMICAL TECHNOLOGY
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Problems solved by technology

At the same time, when traditional MPCA is modeled, firstly, the expansion by batch is limited by the need for batch trajectory synchronization processing and online monitoring to estimate the future data trajectory; main nonlinear limitation

Method used

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  • Method of monitoring faults in sections for intermittent control system
  • Method of monitoring faults in sections for intermittent control system
  • Method of monitoring faults in sections for intermittent control system

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Embodiment Construction

[0021] The present invention will be described in detail below in conjunction with examples.

[0022] The method of the present invention carries out principal component analysis to the data matrix of each sampling moment after multi-batch intermittent process data is expanded according to variables, calculates its corresponding dynamic characteristic transformation index CPV(1), and uses the fuzzy C-means clustering algorithm to analyze the CPV( 1) Fuzzy clustering realizes multi-stage soft partitioning; for each stage after partitioning, an improved MPCA monitoring model with time-varying covariance is established. The principal component analysis is carried out on the data matrix of each sampling time after multi-batch batch process data is expanded according to variables, and the index CPV(1) representing the change of the dynamic characteristics of the data is obtained. Fuzzy C-means clustering is performed on the data dynamic characteristic change index CPV(1) at each sa...

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Abstract

The invention discloses a method of monitoring faults in sections for an intermittent control system and relates to a fault monitoring method. Firstly, a plurality of batches of collected intermittent process data are standardized in a way of expanding variables, and a data matrix on each sampling time is subjected to principal component analysis; secondly, a fuzzy C-means clustering is a fuzzy clustering analysis method which is suitable for soft partition and is generated through combining a fuzzy set theory and a k-means clustering; and thirdly, after segmentation is finished, an improved MPCA (Multiway Principal Component Analysis) model with a time varying principal element covariance on the basis of expanding variables is established on each subphase, then when on-line monitoring is carried out, which phase a new batch of data belongs to is judged, whether the data exceeds the fault monitoring control limit or not is calculated and judged, if so, a fault occurs, and the fault monitoring in sections ends. According to the invention, process multi-phase partition is more accurate, misinformation and missing report rates in monitoring are reduced, and the practical application and operability are strong.

Description

technical field [0001] The invention relates to a fault monitoring method, in particular to a method for segmental fault monitoring of an intermittent control system. Background technique [0002] The batch production process is one of the important production processes in modern industrial production. It has the characteristics of multi-stage, dynamic and complex response, and has been widely used in pharmaceutical production, petrochemical, semiconductor processing and biological products and other industries. Due to the complexity of the operating conditions and reaction mechanism of the batch process, it is difficult to establish a mechanism model with high reliability and accuracy. Therefore, it is important to establish a reasonable and effective multivariate statistical monitoring system to realize the on-line monitoring and fault diagnosis of the batch process. Practical significance. [0003] Multi-directional principal component analysis (MPCA) is a method of bat...

Claims

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Application Information

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IPC IPC(8): G05B23/02
Inventor 张晓丹张新民王国柱
Owner SHENYANG INSTITUTE OF CHEMICAL TECHNOLOGY
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