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csth multiplicative fault diagnosis method based on dynamic principal component analysis

A technology of dynamic principal component analysis and fault diagnosis, which is applied in the field of data analysis, can solve problems such as no multiplicative fault diagnosis, failure to consider the internal mechanism of the process, etc., to reduce the amount of calculation, the diagnosis method is simple and easy, and the possibility of misdiagnosis is reduced sexual effect

Active Publication Date: 2022-03-18
CENT SOUTH UNIV
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Problems solved by technology

In the prior art, the diagnosis is mainly for additive faults, but not for multiplicative faults, and the internal mechanism faults of the process are not considered

Method used

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  • csth multiplicative fault diagnosis method based on dynamic principal component analysis
  • csth multiplicative fault diagnosis method based on dynamic principal component analysis
  • csth multiplicative fault diagnosis method based on dynamic principal component analysis

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Embodiment Construction

[0047]The specific implementation manners of the present invention will be further described in detail below in conjunction with the accompanying drawings and embodiments. The following examples are used to illustrate the present invention, but are not intended to limit the scope of the present invention.

[0048] Such as figure 1 In the continuous stirred tank heater (i.e. CSTH) shown, cold water and hot water are injected into the stirred heating tank, and the mixed water is heated by steam. After heating to the set temperature, it is discharged from the bottom of the tank and is transmitted through the hot water flow rate. The transmitter, cold water flow transmitter, mixed water level transmitter, and mixed water temperature transmitter respectively detect hot water flow, cold water flow, water level in the kettle, water temperature in the kettle, and the input is the valve adjustment of steam and cold water The output is the flow of cold water, the level of water in the ...

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Abstract

The invention discloses a CSTH multiplicative fault diagnosis method based on dynamic principal component analysis, including: S1, data preparation and preprocessing; S2, constructing a dynamic principal component model; S3, constructing a multiplicative fault model, and obtaining reconstruction Error: According to the dynamic principal component model, a multiplicative fault model is constructed, and the fault data after normalization and time series expansion are used to obtain the reconstruction values ​​along the directions of m assumed fault variables based on the optimization objective of minimizing the reconstruction error. The optimal solution of the amplitude value, and then obtain the reconstruction error of the optimal reconstruction matrix of the m assumed fault variable direction; S4, fault variable diagnosis: compare the reconstruction error, the minimum reconstruction error corresponds to The assumed fault variable of is diagnosed as the fault variable. Realize fault diagnosis for multiplicative faults.

Description

technical field [0001] The invention relates to the technical field of data analysis, in particular to a multiplicative fault diagnosis method of CSTH based on dynamic principal component analysis, and the CSTH represents a continuous stirred tank heater. Background technique [0002] With the widespread application of distributed control systems and intelligent instruments in industrial processes, a large amount of process data can be collected and stored. Using data analysis technology to mine process information contained in process data can assist decision-making, guide production operations, and improve process quality. Industrial performance. In particular, as the process system continues to develop toward large-scale development, its accurate mechanism model has become difficult to obtain, and data analysis technology has become an important driving force to promote the development of large-scale industrial systems towards intelligence, digitalization and informationi...

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): G06F30/20G06K9/62
CPCG06F30/20G06F18/2135
Inventor 王凯郭智莹王雅琳袁小锋
Owner CENT SOUTH UNIV
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