Multi-variable fault identification method of industrial process

A fault identification and industrial process technology, applied in the direction of instruments, electrical testing/monitoring, control/regulation systems, etc., can solve the problems of noise information deteriorating the identification effect and inability to make full use of data

Inactive Publication Date: 2015-12-23
CHINA UNIV OF PETROLEUM (EAST CHINA)
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

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Problems solved by technology

Although this method has been successfully applied initially, its disadvantages are: (1) Noise suppression is not considered in the calculation of the dissimilarity index, but the noise information

Method used

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  • Multi-variable fault identification method of industrial process
  • Multi-variable fault identification method of industrial process
  • Multi-variable fault identification method of industrial process

Examples

Experimental program
Comparison scheme
Effect test

Embodiment 1

[0078] Example 1: Take the CSTR system as an example. The CSTR system is a chemical reactor. The solution of material A enters the reactor, and a first-order irreversible chemical reaction occurs to generate material B. The reaction is an exothermic reaction, so it needs to pass through the external clamp. The cooling agent takes away the heat of reaction. In order to ensure the normal operation of the process, it generally has a cascade control system.

[0079] According to the process mechanism, the dynamic mechanism model of the CSTR system is established as follows:

[0080]

[0081]

[0082]

[0083]

[0084] In the formula, A is the cross-sectional area of ​​the reactor, c A is the concentration of material A in the reactor, c AF is the concentration of material A in the feed, C p is the specific heat of the reactants, C pC is the coolant specific heat, E is the activation energy, h is the reactor liquid level, k 0 is the response factor, Q F Feed flow r...

Embodiment 2

[0177] Embodiment 2: Taking a well-known TE chemical process as an example, the TE chemical process has been widely used in fault diagnosis and process control method research. The TE chemical process consists of five units, with a total of 52 process variables, including 11 operating variables, 19 component variables and 22 continuous measurement variables. The flow chart of the TE chemical process is as follows Figure 6 shown. Additional details on TE chemical processes are found in references (J.J. Downs, E.F. Vogel, Aplant-wide industrial process control problem, Computers & Chemical Engineering, 17 (1993) 245-255).

[0178] Simulation data packages for TE chemical processes are available at the website http: / / web.mit.edu / braatzgroup / index.html Upload and download. The data packet contains 1 normal working condition and 21 fault working condition data IDV(1)~IDV(21). The normal working condition data has 500 sampling points and 52 variables. The fault data set is div...

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Abstract

The invention relates to a multi-variable fault identification method of the industrial process. The method comprises the following steps that (1) a normal operation condition data set X and K types of known fault mode data sets of a historical database are collected, the mean value mean(X) and the standard deviation std(X) of the normal operation condition data set are calculated, and the known fault mode data sets are standardized to obtain a new fault mode data set; (2) data windows are constructed under the different fault mode data sets to calculate six types of statistical variables; (3) faults in the process are detected, and real-time fault data S is collected and standardized; (4) principal component dissimilarity analysis is implemented on the basis of the step (3), and the fault identification indexes FRI between a fault data set to be identified and the know fault mode data sets are calculated; and (5) the FRI are ordered to obtain a fault identification result. The method is based on the principal component dissimilarity analysis of the statistical quantity, in dissimilarity analysis, principal component information is extracted, minor data information is abandoned, influence of noise is inhibited, and high-order statistic information can be fully dug.

Description

technical field [0001] The invention belongs to the technical field of industrial process fault identification, and in particular relates to a multivariable industrial process fault identification method based on statistical principal component dissimilarity analysis. Background technique [0002] With the formation of highly integrated and large-scale modern industrial systems, fault diagnosis technology has become a key technology to ensure the safe and stable operation of modern industrial systems. Diagnostic techniques have attracted widespread attention from industrial process engineers and academic researchers. Researchers have proposed a series of data-driven fault diagnosis methods, including Principal Component Analysis (PCA), Independent Component Analysis (ICA), and Partial Least Squares (PLS). Most of the current research on fault diagnosis methods focuses on the problem of fault detection (that is, how to quickly and effectively find process faults), but there ...

Claims

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Application Information

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IPC IPC(8): G05B23/02
CPCG05B23/024
Inventor 邓晓刚徐莹田学民
Owner CHINA UNIV OF PETROLEUM (EAST CHINA)
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