Fault type matching method based on fault feature variable selection

A variable selection, fault type technology, applied in the direction of instruments, electrical testing/monitoring, control/regulation systems, etc.

Active Publication Date: 2019-03-01
NINGBO UNIV
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  • Abstract
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  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

However, under the limited amount of fault training sample data, how to identify these characteristic variables poses a big challenge to solve this problem

Method used

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  • Fault type matching method based on fault feature variable selection
  • Fault type matching method based on fault feature variable selection
  • Fault type matching method based on fault feature variable selection

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Embodiment Construction

[0051] The specific implementation of the method of the present invention will be described in detail below in conjunction with the accompanying drawings.

[0052] The invention discloses a fault type matching method based on fault characteristic variable selection, figure 1 The flow chart of the implementation of fault feature variable selection in the offline modeling stage of the method of the present invention is shown in , and the specific implementation includes steps (1) to (8) as shown below.

[0053] Step (1): Find the sampling data under different fault conditions from the historical database of the production process, and form a type C reference fault data matrix X 1 , X 2 ,...,X C ,in N c is the number of available samples of the c-th fault, subscript c=1, 2, ..., C, R is a set of real numbers, means N c ×m-dimensional real number matrix, m is the total number of process measurement variables.

[0054] Step (2): Collect the N of the production process unde...

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Abstract

The present invention discloses a fault type matching method based on fault feature variable selection. The objective of the invention is to solve the problem that fault feature variable selection isimplemented for each fault type in a historical database on the premise of the limited data amount of each fault training samples, and the feature variables are employed to implement the fault type matching. Especially, the method comprises the steps of: employing neighbor compositions for analysis to find out each fault type available data one by one to compare feature variables with abnormal changes between normal working condition data; employing the fault feature variables to implement the similarity calculation between window matrixes; and finally, distinguishing the fault types accordingto the maximum similarity. The method employs the feature variables of each fault to implement fault type matching to eliminate the interference influence of non-feature variables and directly reducethe variable dimensions so as to allow the problem of the limited sample amount not to be remarkable. Therefore, the method is an optimal fault diagnosis method.

Description

technical field [0001] The invention relates to a data-driven fault diagnosis method, in particular to a fault type matching method based on fault characteristic variable selection. Background technique [0002] Generally speaking, the purpose of monitoring the operating status of the production process is firstly to find faults in a timely and accurate manner, and secondly to identify the root cause or type of faults. Therefore, both fault detection and fault diagnosis are indispensable, and they are of great significance to ensure safe production and maintain stable product quality. Looking at the research results in the field of process monitoring in recent years, there are endless researches on fault detection, but only a handful of research results on fault diagnosis. This phenomenon is particularly prominent in the field of data-driven fault diagnosis research. Compared with the available fault detection methods and technologies, there are few achievements in fault di...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G05B23/02
CPCG05B23/0245
Inventor 皇甫皓宁童楚东俞海珍
Owner NINGBO UNIV
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