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A Dynamic Process Monitoring Method Based on New Dynamic Principal Component Analysis

A technology of dynamic principal component analysis and dynamic process, which is applied in the direction of program control, electrical program control, comprehensive factory control, etc., and can solve problems such as inability to dig out time series correlation

Active Publication Date: 2021-03-09
NINGBO UNIV
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Problems solved by technology

However, the objective function of the PCA algorithm determines that the PCA model can only mine the variance information in the training data, and cannot mine the correlation in the time series

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  • A Dynamic Process Monitoring Method Based on New Dynamic Principal Component Analysis
  • A Dynamic Process Monitoring Method Based on New Dynamic Principal Component Analysis
  • A Dynamic Process Monitoring Method Based on New Dynamic Principal Component Analysis

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Embodiment Construction

[0050] The method of the present invention will be described in detail below in conjunction with the accompanying drawings and specific examples of implementation.

[0051] Such as figure 1 As shown, the present invention discloses a dynamic process monitoring method based on a novel dynamic principal component analysis, and the specific implementation method is as follows.

[0052] Step (1): Collect samples under normal operating conditions of the production process to form a training data matrix X∈R n×m , and calculate the mean μ of each column vector in matrix X 1 , μ 2 ,…,μ m and the standard deviation δ 1 ,δ 2 ,…,δ m , corresponding to the composition mean vector μ=[μ 1 , μ 2 ,…,μ m ] T with standard deviation vector δ=[δ 1 ,δ 2 ,…,δ m ].

[0053] Step (2): According to the formula Standardize the matrix X to get the matrix where U ∈ R n×m It is a matrix composed of n same mean vector μ, that is, U=[μ,μ,…,μ] T , the elements on the diagonal in the diag...

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Abstract

The invention discloses a dynamic process monitoring method based on a novel dynamic principal component analysis, which aims to simultaneously excavate variance features and autocorrelation features in training data, and implement dynamic process monitoring based on this. Specifically, the method of the present invention redefines the objective function of the traditional principal component analysis algorithm, so that the objective function of the new principal component analysis algorithm involved in the present invention covers both variance characteristics and autocorrelation characteristics. Because the method of the present invention considers the variance feature of the data and the autocorrelation feature of the time series at the same time when the data feature is mined. therefore. The new principal component analysis algorithm involved in the method of the present invention is not only a brand-new feature extraction algorithm, but also can mine more comprehensive features. It can be said that the method of the present invention is a more preferred dynamic process monitoring method.

Description

technical field [0001] The invention relates to a data-driven process monitoring method, in particular to a dynamic process monitoring method based on novel dynamic principal component analysis. Background technique [0002] In recent years, with the upsurge of industrial "big data", modern industrial processes are gradually moving towards digital management. This is mainly due to the rapid development and wide application of advanced instrument technology and computing technology. Production process objects can store and measure massive amounts of data offline and online. These data contain information that can reflect the operating status of the production process, and the use of sampled data to monitor the operating status of the process has been favored by many scholars. In the last ten years, both academia and industry have invested a lot of manpower and material resources in the research of process monitoring methods with fault detection and diagnosis as the core task...

Claims

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): G05B19/418
CPCG05B19/41885G05B2219/32339Y02P90/02
Inventor 皇甫皓宁童楚东葛英辉
Owner NINGBO UNIV
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