Fault prediction method based on joint relative change analysis and autoregression model

An autoregressive model, a relative change technique, used in electrical testing/monitoring, testing/monitoring control systems, instruments, etc.

Active Publication Date: 2015-06-17
ZHEJIANG UNIV
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  • Abstract
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  • Application Information

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Problems solved by technology

[0005] The purpose of the present invention is to provide a fault prediction method based on joint relative change analysis and autoregressive model aiming at the deficiencies of existing fault modeling and prediction methods

Method used

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  • Fault prediction method based on joint relative change analysis and autoregression model
  • Fault prediction method based on joint relative change analysis and autoregression model
  • Fault prediction method based on joint relative change analysis and autoregression model

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Embodiment Construction

[0094] The present invention will be described in further detail below in conjunction with the accompanying drawings and specific examples.

[0095]The Tennessee-Eastman Chemical Industrial Process is a complex nonlinear process created by Eastman Chemical Company to provide a realistic industrial process for evaluating process control and monitoring methods. This process includes 4 reactions in total. There are 4 kinds of gas feeds A, C, D, and E to generate two products G, H. In addition, a small amount of inert component B and by-product F are contained in the feed.

[0096] The reaction equation is as follows:

[0097] A+C+D=G

[0098] A+C+E=H (45)

[0099] A+E=F

[0100] 3D = 2F

[0101] The process includes 41 measured variables and 12 controlled variables, the variables are shown in Table 1.

[0102] Table 1 Tennessee-Eastman Process Measured Variables Table

[0103] serial number

variable name

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variable name

serial number

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Abstract

The invention discloses a fault prediction method based on joint relative change analysis and autoregression modeling. According to the method, on the basis of principal component analysis, the fault influences are decomposed based on a joint relative change analysis method, and the fault direction is determined; then the fault amplitude is evaluated based on a reconstruction technology according o the determined fault direction, and data recovery of the normal portion is conducted; new monitoring statistical magnitude D<2> covering the normal data fluctuation is defined, and accordingly the event alarm limit (please see the formula in the specification) is established; finally an autoregression model is established based on the new monitoring statistical magnitude D<2> to be used for predicting the on-line monitoring statistical magnitude, and alarming in advance of the fault is achieved. The fault prediction method is easy and convenient to implement, efficient and free of depending on prior process knowledge and hypothesis. The fault prediction result is significant to subsequent fault diagnosis and repair, process engineers judge the process operation state timely and easily, and thus safe and reliable industrial production and pursuit for high-quality products are guaranteed.

Description

technical field [0001] The invention belongs to the field of complex industrial process fault modeling and diagnosis, and in particular relates to an online process fault prediction method. Background technique [0002] Modern industrial process production equipment is numerous, and the process principle is becoming more and more complex. Real-time fault detection and diagnosis technology plays an important role in ensuring operation safety and improving quality. With the intensification of market competition and the strong demand for safe and reliable operation of the production process, online process monitoring, fault modeling and diagnosis technologies have received more and more attention. In the past few decades, multivariate statistical analysis techniques represented by principal component analysis and partial least squares have been applied in a large number of industrial practices due to their excellent characteristics of not relying on process knowledge and easy o...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G05B23/02
CPCG05B23/024
Inventor 赵春晖秦岩
Owner ZHEJIANG UNIV
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