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Fault diagnosis method through combination of nested iteration Fisher discriminant analysis and relative change

A technology of Fisher discrimination and relative change, which is applied to instruments, electrical test/monitoring, control/regulation systems, etc., and can solve problems such as bias and increased data fluctuations

Active Publication Date: 2016-08-31
ZHEJIANG UNIV
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Problems solved by technology

In fault diagnosis, when comparing a class of fault data with normal data, there are often two fault situations: bias and increased data fluctuation.

Method used

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  • Fault diagnosis method through combination of nested iteration Fisher discriminant analysis and relative change
  • Fault diagnosis method through combination of nested iteration Fisher discriminant analysis and relative change
  • Fault diagnosis method through combination of nested iteration Fisher discriminant analysis and relative change

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Embodiment Construction

[0049] The present invention will be described in further detail below in conjunction with the accompanying drawings and specific examples.

[0050] Take the Tennessee-Eastman process as an example. The Tennessee-Eastman process is a typical chemical process. The variables of this process include: 41 measured variables and 11 operational variables. The variables are listed in Table 1.

[0051] Table 1 Tennessee-Eastman Process Variable Table

[0052]

[0053] Such as figure 1 As shown, a fault diagnosis method combining nested iterative Fisher discriminant analysis and relative change analysis proposed by the present invention includes the following steps:

[0054] Step 1: Acquire data: For a chemical process with J variables, each sampling can obtain a 1×J vector, and the data obtained after sampling K times can be described as a two-dimensional matrix X(K×J). Get the normal data X respectively n (N n ×J) and fault data X f,m (N f,m ×J), where the subscript n repres...

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Abstract

The invention discloses a fault diagnosis method through combination of nested iteration Fisher discriminant analysis and relative change. Two types of faults-bias and data volatility often simultaneously exist in one type of fault data. According to the method, fault information is extracted through application of combination of nested iteration Fisher discriminant analysis and relative change analysis for the two types of faults of the same fault data, and then a reconstruction model is determined according to the fault information of each type of fault data and used for online fault diagnosis. According to the method, the disadvantages that the fault characteristics cannot be fully extracted by single method can be overcome, the performance of online fault diagnosis can be greatly enhanced and the faults can be accurately and rapidly restored through assistance so that process safety can be guaranteed and production benefit can be enhanced.

Description

technical field [0001] The invention belongs to the field of chemical process statistical monitoring, in particular to a fault diagnosis method combining nested iterative Fisher discriminant analysis and relative change analysis. Background technique [0002] With the advancement of science and technology, the chemical process production system is becoming more and more complex. In order to ensure process safety and improve production efficiency, effective fault detection and fault diagnosis methods must be adopted. Fault detection is to monitor the operation of the process and issue an alarm in time when abnormal conditions occur; fault diagnosis acts on the alarm signal to determine the type of fault and process the abnormal signal. With the development of technology, it is becoming more and more convenient to obtain data in the industrial field, so the fault diagnosis method based on data has become a research hotspot. [0003] Predecessors have made corresponding resea...

Claims

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Application Information

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IPC IPC(8): G05B23/02
CPCG05B23/0243
Inventor 赵春晖王玥
Owner ZHEJIANG UNIV
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