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Principal component tracking based industrial fault monitoring method and application thereof

A principal component tracking, fault detection technology, applied in general control systems, electrical testing/monitoring, testing/monitoring control systems, etc., can solve problems such as high false alarm rate, and achieve the effect of improving the effect

Active Publication Date: 2015-07-22
ZHEJIANG UNIV
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Problems solved by technology

Theoretically, fault detection can be performed by observing the amplitude of elements in a sparse matrix, but due to noise interference, doing so will lead to a relatively high false alarm rate

Method used

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  • Principal component tracking based industrial fault monitoring method and application thereof
  • Principal component tracking based industrial fault monitoring method and application thereof
  • Principal component tracking based industrial fault monitoring method and application thereof

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Embodiment

[0071] As one of the most important basic industries in the national economy, iron and steel smelting is an important indicator to measure a country's economic level and comprehensive national strength. Blast furnace ironmaking is the most important link in the production process of the iron and steel industry, so it is of great significance to study the abnormal working condition diagnosis and safe operation methods of large blast furnaces.

[0072] The blast furnace is a huge airtight reaction vessel, and its internal smelting process is a typical "black box" operation through a series of complex physical, chemical and heat transfer reactions under high temperature and high pressure conditions. It is precisely because of the complexity inside the blast furnace that the data collected are diverse, linear, nonlinear, non-Gaussian and dynamic. Therefore, our proposed method is effective for the diagnosis of blast furnace faults. The effectiveness of the method of the present i...

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Abstract

The invention discloses a principal component tracking based industrial fault monitoring method and application thereof, and belongs the technical field of industrial process monitoring and diagnosing. The method includes decomposing industrial collected data into a low rank matrix including complete process operation information and a sparse matrix including sensor noise and process faults according to a principal component tracking method; performing fault detection in the low rank matrix by the aid of a statistical magnitude T2, and performing fault detection in the sparse matrix by the aid of a statistical magnitude of a mean value correlation coefficient. Compared with the prior art, the principal component tracking based industrial fault monitoring method has the advantages that according to the characteristic that the matrixes are decomposed by the principal component tracking method, different statistical magnitudes are adopted for the matrixes with different characteristics, effective information included in the data are fully utilized, and accordingly, high accuracy in industrial fault detection is achieved.

Description

technical field [0001] The invention belongs to the field of industrial process monitoring and fault diagnosis, in particular to an online industrial fault detection based on principal component tracking, using T 2 Statistics and Mean Correlation Coefficient Statistics. Background technique [0002] Industrial process production is the pillar industry of national economic development, so it is very important to ensure the efficiency and stability of the production process. Fault detection is a key step to achieve this goal. [0003] There are many traditional fault detection methods, such as principal component analysis (PCA), partial least squares (PLS), support vector machine (SVM), artificial neural network (ANN) and improved methods based on them. The principal component tracking method has the advantages of being insensitive to outliers, sensitive to faults caused by small changes in variables, and can solve nonlinear processes. The essence of the principal component ...

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G05B23/02
CPCG05B23/0232
Inventor 杨春节潘怡君王琳孙优贤安汝峤
Owner ZHEJIANG UNIV
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