Method for performing fault diagnosis by using model conversion

A fault diagnosis and model conversion technology, applied in the direction of calculation models, special data processing applications, and based on specific mathematical models, can solve problems such as difficulties in Bayesian network construction, improve diagnostic accuracy, ensure versatility, and clearly understand problem effect

Active Publication Date: 2010-08-25
BEIHANG UNIV
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Problems solved by technology

[0004] In order to overcome the difficulty of building Bayesian networks in the field of fault diagnosis and expand the use of Bayesian networks in the field of fault diagnosis, the present invention proposes a method for fault diagnosis using model conversion, which can While ensu...

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  • Method for performing fault diagnosis by using model conversion
  • Method for performing fault diagnosis by using model conversion
  • Method for performing fault diagnosis by using model conversion

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Embodiment Construction

[0019] The present invention will be further described below in conjunction with the accompanying drawings and embodiments.

[0020] In order to facilitate those of ordinary skill in the art to understand and implement the present invention, the present invention will be described in further detail below in conjunction with the accompanying drawings. It should be understood that the embodiments described here are only used to illustrate and explain the present invention, and are not intended to limit the present invention. invention.

[0021] Figure 1 describes the detailed process of transforming the failure mode effect analysis model (FMEA model) into a Bayesian network model. The specific steps are:

[0022] (1) The FMEA data needs to be converted into a custom FMEA data structure, and at the same time, a Bayesian network (BN) node list is established, which does not contain node data at this time. In the FMEA data, each piece of data includes failure cause, failure mode, ...

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Abstract

The invention discloses a method for performing fault diagnosis by using model conversion. According to the method, processed related information of a fault mode effect analysis model is converted into a corresponding Bayesian network model by using a self-defined data structure while ensuring complete data, an elementary event, a logic gate and an intermediate event of a fault tree in a fault tree analysis model are converted into nodes in a Bayesian network respectively, and a corresponding conditional probability table in the Bayesian network is set. The fault diagnosis is performed through the converted Bayesian network model. The method of the invention expands the use of the Bayesian network model in the fault diagnosis, improves the diagnosis accuracy of a fault diagnosis model in practical application, ensures the universality of model conversion, and can realize cross-tool conversion among different fault mode effect analysis, fault tree analysis result and the generated Bayesian network.

Description

technical field [0001] The invention belongs to the technical field of fault diagnosis and analysis, and relates to a conversion method between fault diagnosis models, in particular to a fault diagnosis method using the conversion between fault diagnosis models. Background technique [0002] Uncertainty is one of the key technologies to be solved in equipment fault diagnosis, especially for large and complex equipment, there are many intricate and coupled relationships between components and within components, and uncertain factors and uncertain information are full of them. , its faults may be complex forms such as multiple faults and associated faults. Bayesian network can integrate qualitative information and quantitative information, can make full use of pre-test information and test information, and can realize a complete probabilistic description of system failures in the case of incomplete data sets. In addition, the Bayesian network has the ability to describe the n...

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Application Information

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IPC IPC(8): G06F19/00G06N7/00
Inventor 杨顺昆陆民燕郝伯男
Owner BEIHANG UNIV
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