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Improved kernel entropy component analysis based nonlinear fault detection method and application thereof

A nuclear entropy component analysis and fault detection technology, applied in general control systems, comprehensive factory control, instruments, etc., can solve problems that cannot be well satisfied

Inactive Publication Date: 2016-11-09
ZHEJIANG UNIV
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Problems solved by technology

However, the confidence limit calculation methods of these two statistical indicators all assume that the input data obeys a Gaussian distribution, but considering the nonlinearity of the process, this assumption cannot be well satisfied in the actual production process

Method used

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  • Improved kernel entropy component analysis based nonlinear fault detection method and application thereof
  • Improved kernel entropy component analysis based nonlinear fault detection method and application thereof
  • Improved kernel entropy component analysis based nonlinear fault detection method and application thereof

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Embodiment

[0068] my country is a resource and energy-shortage country, and the dependence of the paper industry on imported fiber raw materials is as high as 40%. For chemical mechanical pulping, the beating process is the main process of fiber formation. The beating and refining machine is a closed reflection environment, and the beating is realized through a series of complex physical and chemical processes inside. The data generated in the beating process has the characteristics of nonlinearity. Based on this, the fault detection method proposed by the present invention is adaptable to the fault diagnosis of the beating process. The effectiveness of the invention will be described below in conjunction with Gold East Paper (Jiangsu) Co., Ltd. (hereinafter referred to as Gold East Paper).

[0069] Next, in conjunction with this specific process, the implementation steps of the present invention are described in detail:

[0070] Step 1: Build the model offline, and use the data under...

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Abstract

The invention discloses an improved kernel entropy component analysis based nonlinear fault detection method and an application thereof. According to the method, monitoring for the industrial generation process is realized through combining a Kernel entropy component analysis method and integrated learning and Bayesian interference so as to judge whether a fault occurs in the production process or not. In allusion to a nonlinear problem of data in the practical production process, kernel entropy component analysis is adopted, consideration for actual distribution conditions such as Gaussian distribution and non-Gaussian distribution of data is effectively avoided by using characteristics of information entropy, and multiple models are introduced through integrated learning, so that a problem of kernel function parameter blind selection in kernel entropy component analysis is avoided. The detection effect of faults in the industrial generation process is effectively improved.

Description

technical field [0001] The invention belongs to industrial production process detection and is a fault detection method, in particular to a nonlinear fault detection method based on improved nuclear entropy component analysis. Background technique [0002] With the continuous expansion of production scale, modern industry pays more attention to the safety of the production process. Fault diagnosis is used to monitor the production process. It can detect the abnormal working conditions of the production process and thus determine the root cause. Therefore, multivariate statistical process monitoring has been extensively studied as a fault diagnosis method. [0003] Principal Component Analysis (PCA) is a typical multivariate statistical process monitoring method, which is widely used in pattern recognition, image processing and process monitoring. This method achieves the purpose of dimensionality reduction by extracting the principal components of the process, which are ref...

Claims

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Application Information

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IPC IPC(8): G05B19/418
CPCY02P90/02G05B19/41885G05B2219/32339
Inventor 秦家祥杨春节刘文辉孙梦园
Owner ZHEJIANG UNIV
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