Model-based aircraft system fault positioning method

A fault location and aircraft system technology, applied in aircraft maintenance, special data processing applications, geometric CAD, etc., can solve problems such as difficult case revision consistency inspection, inability to interpret diagnosis results, and difficulty in obtaining case knowledge, so as to improve fault Diagnosis efficiency, saving manpower, avoiding the effect of model analysis process
CN112597585AInactive Publication Date: 2021-04-02CHENGDU AIRCRAFT DESIGN INST OF AVIATION IND CORP OF CHINA
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Patent Information

Authority / Receiving Office
CN Β· China
Patent Type
Applications(China)
Current Assignee / Owner
CHENGDU AIRCRAFT DESIGN INST OF AVIATION IND CORP OF CHINA
Publication Date
2021-04-02
Estimated Expiration
Not applicable Β· inactive patent

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Abstract

The invention belongs to the field of aircraft complex system fault diagnosis design, and particularly relates to a method for achieving LRU-level rapid fault positioning of an aircraft system. The aircraft system fault diagnosis method based on the model is provided and used for rapidly achieving LRU-level fault positioning after an outfield aircraft system breaks down, so the replacement of a fault unit is rapidly completed, and finally the integrity rate and the attendance rate of an aircraft are increased.
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Description

technical field

[0001] The invention belongs to the field of aircraft complex system fault diagnosis design, and in particular relates to a method for realizing LRU-level fast fault location for aircraft systems. Background technique

[0002] At present, the existing aircraft system fault diagnosis methods mainly include: rule-based reasoning, artificial neural network-based and case-based methods, etc. There is a knowledge acquisition bottleneck in the rule-based diagnosis method. The connection chain between the symptoms observed by the system and the corresponding faults is often strengthened with the increase of the complexity of the system. The rule of expert experience is often not unique, and There is considerable difficulty. Neural network-based diagnostic methods usually require more training examples for neural network learning in order to make the network converge and obtain stable diagnostic results, and cannot make reasonable explanations for the diagnostic res...

Claims

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