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Multi-block PCA Fault Monitoring Method Based on Fault Sensitive Pivot Component Selection

A fault monitoring and fault technology, applied in the direction of electrical testing/monitoring, testing/monitoring control systems, instruments, etc., to achieve the effect of avoiding computing resources

Active Publication Date: 2020-06-05
JIANGNAN UNIV
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  • Application Information

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Problems solved by technology

[0006] Aiming at the problem of how to select the principal component in the traditional PCA fault monitoring algorithm, a multi-block PCA fault monitoring method based on fault-sensitive pivot selection is proposed

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  • Multi-block PCA Fault Monitoring Method Based on Fault Sensitive Pivot Component Selection
  • Multi-block PCA Fault Monitoring Method Based on Fault Sensitive Pivot Component Selection
  • Multi-block PCA Fault Monitoring Method Based on Fault Sensitive Pivot Component Selection

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Embodiment Construction

[0025] Combine below figure 2 As shown, the present invention is further detailed:

[0026] Take the common chemical process-TE process and a numerical example as an example. The two types of faults set in the numerical example and the 21 types of faults in the TE process were monitored. TE process is a simulation system proposed by Tennessee Eastman Chemical Company based on an actual chemical production process. In the field of process system engineering, TE process is a commonly used standard problem (Benchmark problem), which simulates the actual complex industry well. Many typical characteristics of process systems are therefore widely used as simulation examples in the research of control, optimization, process monitoring and fault diagnosis. TE process mainly consists of five main units: reactor, condenser, compressor, separator and stripper. The process includes 22 process measurement variables, 19 component measurement variables, and 12 operating variables. In this p...

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Abstract

The invention discloses a monitoring method for multi-block PCA faults based on fault-sensitive principle element selection. Aiming at the problem of how to select a principle element in a traditionalPCA fault monitoring algorithm, the invention defines a fault-sensitive coefficient epsilon as a new principle element sorting criterion; the principle element has m sorting results based on an m-dimensional variable; and each sorting result is divided into a sub-block. The principle element of which the fault sensitive coefficient epsilon is more than a threshold [epsilon]lim is selected in eachsub-block to perform fault monitoring, and T2 statistics of each sub-block are computed. Then, monitoring results of the sub-blocks are fused by using a Bayesian inference method, and a final BIC monitoring result is acquired. According to the method provided by the invention, on the one hand, the principle element can be extracted without depending on a fault data set, and on the other hand, computing resources consumed by real-time modeling are avoided.

Description

Technical field [0001] The invention relates to a multi-block PCA fault monitoring method based on fault-sensitive principal component selection, and belongs to the field of complex industrial process modeling and fault diagnosis. Background technique [0002] The scale of modern industrial production is increasing, and the complexity of the process is increasing. In order to ensure the smooth operation of the production process, improve production efficiency and product quality, monitoring the production process has become very important. [0003] Based on this background, the multivariate statistical method (MSPM) has been widely used in the field of process monitoring. Among the common multivariate statistical process monitoring methods are principal component analysis (PCA), partial least squares (PLS), and independent component analysis (ICA). The PCA method is the most commonly used algorithm in the field of fault monitoring. It can reduce the dimensionality of data, elimina...

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): G05B23/02
CPCG05B23/024
Inventor 熊伟丽顾炳斌马君霞
Owner JIANGNAN UNIV