Nonlinear process fault identification method and system based on discriminant kernel slow characteristic analysis

A fault identification and slow feature technology, applied in character and pattern recognition, comprehensive factory control, instruments, etc., can solve the problems of not being able to use category label information, not being able to judge fault types, and reducing fault diagnosis performance, so as to improve fault identification performance Effect

Active Publication Date: 2020-03-13
SHANDONG JIANZHU UNIV
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Problems solved by technology

Although the method based on KSFA has achieved certain application results, the inventors found that the disadvantages of the KSFA method are: (1) KSFA is an unsupervised algorithm that cannot use the category label information of the process data, which reduces the performance of fault diagnosis
(2) KSFA has been used to detect faults in nonlinear processes. It is only used to detect faults, but it is still unable to judge the type of faults.

Method used

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  • Nonlinear process fault identification method and system based on discriminant kernel slow characteristic analysis
  • Nonlinear process fault identification method and system based on discriminant kernel slow characteristic analysis
  • Nonlinear process fault identification method and system based on discriminant kernel slow characteristic analysis

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Embodiment 1

[0031] Such as figure 1 and figure 2 As shown, this embodiment provides a nonlinear process fault identification method based on discriminant kernel slow feature analysis, which includes:

[0032] Step S101: Based on minimizing the time variation of the normal working condition data set and maximizing the inter-class separability between the normal working condition data set and the unidentified fault data set, the unidentified fault data is extracted using the discriminant core slow feature analysis algorithm set and the failure direction of the historical failure mode dataset.

[0033] In the specific implementation, before extracting the fault direction of the fault data set to be identified and the historical fault mode data set, it also includes:

[0034] Receive the normal working condition data set and the historical failure mode data set, standardize the normal working condition data set, standardize the historical failure mode data set according to the normal worki...

Embodiment 2

[0145] This embodiment provides a nonlinear process fault identification system based on discriminant kernel slow feature analysis, which includes:

[0146] (1) Fault direction extraction module, which is used to minimize the time variation of the normal working condition data set and maximize the inter-class separability between the normal working condition data set and the fault data set to be identified, using the discriminant kernel slow The feature analysis algorithm extracts the fault direction of the fault data set to be identified and the historical fault mode data set.

[0147] Specifically, the process of using the discriminant core slow feature analysis algorithm to extract the fault direction of the fault data set to be identified and the historical fault mode data set is as follows:

[0148] Using the discriminant kernel slow feature analysis algorithm to construct the discriminant kernel slow feature analysis objective function, solve the minimization of the disc...

Embodiment 3

[0164] This embodiment provides a computer-readable storage medium, on which a computer program is stored, and when the program is executed by a processor, the figure 1 and figure 2 The steps in the non-linear process fault identification method based on discriminant kernel slow feature analysis are shown.

[0165] This embodiment overcomes the problem that the traditional contribution graph method is prone to "smearing" effect and the limitation that it is not suitable for nonlinear process monitoring methods, and uses the discriminant core slow feature analysis algorithm to extract the fault data set to be identified and the historical fault mode data By calculating the similarity between the fault directions to identify the fault types of the data to be identified, it provides a new idea for identifying the fault types of nonlinear industrial processes based on the discriminant kernel slow feature analysis method, and achieves the goal of using process data. The purpose o...

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Abstract

The invention provides a nonlinear process fault identification method and system based on discriminant kernel slow characteristic analysis. The nonlinear process fault identification method based ondiscriminant kernel slow characteristic analysis comprises the steps of extracting fault directions of a to-be-identified fault data set and a historical fault mode data set by utilizing a discriminant kernel slow feature analysis algorithm according to minimization of time change of a normal working condition data set and maximization of inter-class separability between the normal working condition data set and the to-be-identified fault data set at the same time; and comparing the similarity between the fault direction of the to-be-identified fault data set and the fault direction of the historical fault mode data set, and determining the fault type of the to-be-identified fault data according to the similarity coefficient. Class label information of nonlinear process data can be utilized, and the fault identification performance of the nonlinear process is improved.

Description

technical field [0001] The invention belongs to the field of multi-variable nonlinear industrial process fault identification, in particular to a nonlinear process fault identification method and system based on discriminant kernel slow feature analysis. Background technique [0002] The statements in this section merely provide background information related to the present invention and do not necessarily constitute prior art. [0003] Modern industrial systems are increasingly large-scale, integrated, and complex. Fault diagnosis of industrial processes has become a key technology to ensure safe, reliable, trouble-free and stable operation of modern industrial systems. With the wide application of computer control systems, rich process operation data can be collected and stored in real time in industrial processes, and data-driven fault diagnosis technology has gradually become a research hotspot in the field of industrial process monitoring. Researchers have proposed a s...

Claims

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Application Information

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IPC IPC(8): G06K9/62
CPCG06F18/22G06F18/213G06F18/241Y02P90/02
Inventor 张汉元张汉营梁泽宇孙雪莹
Owner SHANDONG JIANZHU UNIV
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