Bayesian-network-based sensor fault diagnosis method in complex system

A Bayesian network, sensor fault technology, applied in general control systems, control/regulation systems, testing/monitoring control systems, etc., can solve problems such as the uncertainty of fault feature models, difficult fault diagnosis, and incomplete information.

Active Publication Date: 2015-08-26
CHONGQING UNIV
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  • Abstract
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  • Claims
  • Application Information

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Problems solved by technology

However, there are many intricate and coupled relationships among the various components, which lead to the characteristics of model uncertainty and incomplete information in the fault characteristics, and it is difficult to make an accurate diagnosis of the fault only by relying on a single information source.

Method used

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  • Bayesian-network-based sensor fault diagnosis method in complex system
  • Bayesian-network-based sensor fault diagnosis method in complex system
  • Bayesian-network-based sensor fault diagnosis method in complex system

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Embodiment Construction

[0018] The preferred embodiments of the present invention will be described in detail below with reference to the accompanying drawings.

[0019] figure 1 It is a flowchart of the method of the present invention, the method may further comprise the steps:

[0020] S1: Model each sensor in the nonlinear dynamic subsystem of complex sensors, and use multi-agents to realize sensor data communication. The jth sensor uses S(I){j},j∈{1,... ,m I} represents the relationship between Bayesian network nodes such as figure 2 Shown; define S(I){j} as formula (1):

[0021] y j ( I ) ( t ) = C j ( I ) x ( I ) ( t ) ...

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Abstract

The invention provides a bayesian-network-based sensor fault diagnosis method in a complex system. A sensor monitoring model is constructed in a complex system; and a dynamic Bayesian network model is applied to a sensor fault diagnosis of the complex system. To be specific, the method comprises the following steps: step one, establishing a single sensor dynamic model S<(I,q)> of a complex system according to all sensor characteristics; step two, establishing a Bayesian network model for the complex system; step three, selecting a non-linear observer sigma <(I,q)>, constructing a sub system estimation model, and obtaining a sensor residual error; step four, estimating a residual error threshold value and determining prior probability distribution of observation node parameters; step five, carrying out updating and adjusting on a Bayesian network structure and parameters and constructing a new Bayesian model; and step six, realizing the sensor fault diagnosis method in the complex system based on the Bayesian network. According to the method, expansion is carried out based on the Bayesian network; and for the complex system, the method has obvious advantages of the complex system fault diagnosis on the condition of information incompleteness.

Description

technical field [0001] The invention relates to a complex system sensor fault diagnosis technology, in particular to a sensor fault diagnosis method in a complex system based on a Bayesian network. Background technique [0002] With the development of modern large-scale mechanical equipment in the direction of large-scale, complex and precise, the structure of components is becoming more and more precise. However, there are many intricate and coupled relationships among the various components, which lead to the characteristics of model uncertainty and incomplete information in the fault characteristics, and it is difficult to make an accurate diagnosis of the fault only by relying on a single information source. In the actual production process, before the accident occurs, the control system often has a warning sign of failure. If the warning sign can be detected in time and controlled, the accident can be completely avoided. The emergence of fault diagnosis and fault toler...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G05B23/02
CPCG05B23/0218
Inventor 屈剑锋柴毅邢占强赵卫峰陈军
Owner CHONGQING UNIV
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