A Non-Gaussian Process Monitoring Method Based on Known Data Regression

A known data and process monitoring technology, applied in the field of data-driven process monitoring, can solve the problem of estimated values ​​generated by data models, achieve the effect of accurate normal areas and improve fault detection capabilities

Active Publication Date: 2021-05-18
吴晓冬
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  • Abstract
  • Description
  • Claims
  • Application Information

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Problems solved by technology

This method is feasible but the difficulty lies in how to generate estimates through the data model

Method used

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  • A Non-Gaussian Process Monitoring Method Based on Known Data Regression
  • A Non-Gaussian Process Monitoring Method Based on Known Data Regression
  • A Non-Gaussian Process Monitoring Method Based on Known Data Regression

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Embodiment Construction

[0071] The method of the present invention will be described in detail below in conjunction with the accompanying drawings and specific examples of implementation.

[0072] Such as figure 1 As shown, the present invention discloses a non-Gaussian process monitoring method based on known data regression. The specific implementation process of the method of the present invention and its superiority over existing methods will be described below in conjunction with an example of a specific industrial process.

[0073] Design a non-Gaussian process with 5 measured variables according to the following formula:

[0074]

[0075] Among them, the source signal s=[s 1 ,s 2 ] is generated according to the formula shown below:

[0076]

[0077] In the above two formulas (19) and (20), the measurement noise v follows a Gaussian distribution with a mean of 0 and a standard deviation of 0.2, t 1 with t 2Both are random numbers uniformly distributed on the interval [0, 1]. Accord...

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Abstract

The invention discloses a non-Gaussian process monitoring method based on known data regression, aiming at converting non-Gaussian independent element components into error information of Gaussian distribution, so as to realize accurate process monitoring of non-Gaussian process objects. Specifically, the method of the present invention first utilizes the independent element analysis (ICA) algorithm to mine the independent element components of the non-Gaussian process object, and then uses known data regression (KDR) to estimate the independent element under the condition of missing variable data one by one. Finally, the process monitoring based on the PCA model is implemented by using the independent element estimation error. Generally speaking, the ICA algorithm can reveal the essence of the original data, and the estimation error obtained through KDR based on the ICA model obeys the Gaussian distribution. Benefiting from the Gaussian distribution characteristic of the error, the normal region described by the method of the present invention is more accurate, and can significantly improve the fault detection capability of the traditional ICA model for non-Gaussian process monitoring.

Description

technical field [0001] The invention relates to a data-driven process monitoring method, in particular to a non-Gaussian process monitoring method based on known data regression. Background technique [0002] The implementation of online fault detection is the basic means to ensure production safety and maintain product quality stability. The research on fault detection is accompanied by the development of the entire production industry. The current fault detection methods can be roughly divided into two categories, one is the fault detection method based on the mechanism model, and the other is the fault detection method based on the data. The fault detection method based on the mechanism model relies on the error between the actual value of some variables or parameters of the process and the estimated value deduced according to the model to implement fault detection. That is to say, how to generate errors is the core of designing fault detection methods based on mechanism...

Claims

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): G06F17/18
CPCG06F17/18
Inventor 孟生军童楚东朱莹
Owner 吴晓冬
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