DR synthetic diagnosing method based on fuzzy fault diagnosis and relevancy model diagnosis

Abstract

The invention provides a DR synthetic diagnosing method based on fuzzy fault diagnosis and relevancy model diagnosis, which is used for electronic equipment fault diagnosis. The method comprises the steps of firstly determining a state parameter type (monitored at each testing point) as a continuous quantity or a logic quantity, wherein in the testing to the continuous quantity, the testing is used as a fault symptom, and a fuzzy relationship matrix R is established; in the testing to the logic quantity, a relevancy matrix D is established, and the established fuzzy relationship matrix R is transposed and is combined with the relevancy matrix as a DR composite matrix; and finally positioning a fault source by using the DR composite matrix and a DR synthetic diagnosis equation aiming at a real-time testing fault state vector. The continuous quantity and the logic quantity in a system state parameter are considered, the roughness in the fuzzy fault diagnosis is made up, the limitation in the relevancy model diagnosis is improved, the method is applicable to the fault diagnosis to the system with the continuous quantity and the logic quantity, and the accurate positioning to the fault source is realized.

Description

technical field [0001] The invention relates to a DR composite diagnosis method for electronic products based on fuzzy fault diagnosis and correlation model diagnosis, and belongs to the technical field of electronic equipment fault diagnosis. Background technique [0002] Information fusion refers to the multi-level and multi-faceted processing of information and data from single and multiple sensors, including: automatic detection, association, correlation, estimation, and combination. The fault detection and location of avionics equipment has always been a very complicated and difficult task. It is necessary to consider various fusion methods such as monitoring data and monitoring data, BIT information and monitoring data, etc., to achieve more intelligent information fusion enhanced diagnosis. Its main information fusion fault diagnosis methods include: fuzzy fault diagnosis, correlation model diagnosis, Bayes reasoning, D-S evidence reasoning and neural network informat...

AI Technical Summary

Problems solved by technology

[0004] Since most systems are digital-analog hybrid circuits, and their state parameters have both continuous and logical quantities, when fuzzy fault diagnosis methods or correlation model diagnosis methods are used for diagnosis, the diagnostic results obtained are not accurate or optimal.

Therefore, for the fault diagnosis of systems with both continuous and logical quantities, there is still a lack of comprehensive diagnostic methods that can not only make up for the roughness of fuzzy fault diagnosis, but also improve the limitations of correlation model diagnosis.

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