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Hybrid fault diagnosis method based on qualitative signed directed graph in petrochemical process

A diagnostic method and fault technology, applied in the direction of instruments, control/regulation systems, general control systems, etc.

Active Publication Date: 2012-11-28
CHINA PETROLEUM & CHEM CORP +1
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  • Abstract
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  • Claims
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Problems solved by technology

[0010] Since fault identification and diagnosis are faced with changeable and complex process systems, at present, there is no method that can be universally applicable to the needs of various fault diagnosis in various industries.

Method used

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  • Hybrid fault diagnosis method based on qualitative signed directed graph in petrochemical process
  • Hybrid fault diagnosis method based on qualitative signed directed graph in petrochemical process
  • Hybrid fault diagnosis method based on qualitative signed directed graph in petrochemical process

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Embodiment Construction

[0057] The present invention proposes a hierarchical diagnosis model that can be quickly established and expressed easily. The hierarchical model is composed of three layers, as shown in the attached figure 1 shown.

[0058] 1) The first layer is an expert system module

[0059] Extract the symptoms of the key nodes of the process flow (obtained based on operating experience) in the fault state, and store them in the expert knowledge base. During real-time monitoring, if the state of these nodes just falls into the state defined by the knowledge base, it can be concluded that it has entered a certain fault state, and the cause and consequence can be determined. In this way, the abnormal working condition management system can directly obtain the abnormal working condition conclusion of the monitored process. At this time, the software system does not need to enter the reasoning algorithm behind, which greatly reduces the reasoning time of the system.

[0060] 2) The second ...

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Abstract

The invention relates to a hybrid fault diagnosis method based on qualitative signed directed graph (SDG) in the petrochemical process. The method comprises the following steps: the characteristic data of the key nodes in a fault state of the process flow are extracted to store in a expert knowledge base; the fuzzy logic and the principle component analysis (PCA) are combined to obtain the hybridalgorithm of fault diagnosis based on SDG; for a built SDG model, the hazard and operability analysis (HAZOP) is performed through automated reasoning; and the analysis result is stored in a fault knowledge base in the expert knowledge modes of fault sign, fault cause, propagation path, unfavourable result and treatment measure. A hybrid expert knowledge system is mainly composed of an expert system and HAZOP analysis results. By adopting the method of the invention, the problems of the fault diagnosis technology in fault detection and diagnosis speed, diagnosis completeness and accuracy, diagnosis resolution and robustness and the like can be solved.

Description

technical field [0001] The invention relates to a fault diagnosis method in a petrochemical process, in particular to a fault diagnosis method based on qualitative SDG mixed fuzzy logic and principal component analysis. Background technique [0002] In the petrochemical production process, due to sensor drift, equipment failure, process fluctuations or operational errors, etc., abnormal working conditions often occur during production and operation, which may affect product quality and production scheduling plans, or cause production accidents and cause casualties and huge economic losses. [0003] How to dig out effective information from massive production data, identify abnormal working conditions in a timely manner, find out the causes of abnormal working conditions, predict the possible consequences of the abnormal working conditions, propose corresponding measures, and carry out Effective management of abnormal working conditions to avoid serious consequences is an im...

Claims

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): G05B23/00
Inventor 牟善军张卫华姜春明王春利李传坤姜巍巍
Owner CHINA PETROLEUM & CHEM CORP
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