Fault detection method and system

A fault detection and fault technology, applied in the field of fault detection methods and systems, can solve problems such as complex design methods and algorithms, limited application range, and inability to meet DAG, so as to achieve the effect of improving the possibility

Active Publication Date: 2013-07-10
广东麦德克斯科技有限公司
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  • Claims
  • Application Information

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Problems solved by technology

However, many relationship structures are complex, and it is not easy to establish a DAG structure, because Bayesian networks are more complex than Naive Bayesian, and it is extremely difficult to construct and train a good Bayesian network
At the same time, the relationship between many factors, such as the above

Method used

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  • Fault detection method and system
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  • Fault detection method and system

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[0030] The present invention will be further described in detail below in conjunction with the accompanying drawings.

[0031] The present invention abandons the method of establishing a directed acyclic graph (DAG) such as Markov Chain Monte Carlo Gibbs Sampling Algorithm to calculate the probability of Bayesian network nodes, but applies Naive Bayes theory. In order to speed up the calculation in the present invention, a relational database can be used. For the naive Bayes theory, the premise of adopting the naive Bayes theory is to assume that each feature of the sample is not related to other features, but this assumption is often invalid in practical applications. In order to apply the naive Bayes theory, the present invention fully converges the mutually influential fault phenomenon nodes directly related to the fault conclusion, thereby simplifying the judgment relationship to conform to the naive Bayes theory. For examples of child node convergence methods, see figure 1...

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Abstract

The invention relates to a fault detection method and system. The fault detection method comprises a first step of inputting fault phenomena; a second step of fully converging fault phenomenon nodes influencing each other and directly associated with fault conclusions, and establishing a Naive Bayesian network; a third step of utilizing a Bayesian formula to detect the credibility grading of all fault conclusions and the fault phenomena; and a fourth step of sequencing all the fault conclusions from high to low according to credibility. The faults do not need to be judged by establishing a directed acyclic graph, fault detection possibility is improved, and the fault detection method and system are particularly suitable for detecting the faults with a plurality of mutually influenced phenomena.

Description

technical field [0001] The invention relates to the field of fault detection, in particular to a fault detection method and system. Background technique [0002] In order to perform fault detection, expert systems are often provided in the prior art to help people perform some detections. The expert system in the prior art is based on the correlation between phenomenon A and conclusion B, using the Bayesian formula (Formula 1) to calculate the correlation probability, and then sorting. [0003] The Bayes formula is defined as follows: [0004] P ( B i / A ) = P ( A / B i ) · P ( B ...

Claims

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Application Information

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IPC IPC(8): G06F19/00
Inventor X·Q·李
Owner 广东麦德克斯科技有限公司
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