An Intelligent Diagnosis Method for Aeroengine Structural Faults

Abstract

The present invention provides a multi-technology fusion aero-engine fault intelligent diagnosis method. A data analysis method is employed to perform identification of fault data samples to match faults which has the similarity with samples to be subjected to analysis in a fault database being not lower than a determination value as suspected faults. According to the typical fault factor decision table, all the suspected faults are subjected to multi-time screening to obtain the limited number of mainly suspected faults with the maximum possibility. A pattern identification algorithm is employed to perform pattern identification of the mainly suspected faults, learning training samples when identification are from fault data features in a fault sample data feature library, identification features when identification are features of samples to be identified, and identification results are subjected to one or more than one kinetics feature inspection. The aero-engine structure-type fault intelligent diagnosis method can selectively determine the learning object identified by the mode to reduce the learning range, the recall ratio [Eta] is reduced from 1 to 33%, and the learning time is reduced from 90s to 19s. Through the inspection link, the identification result is inspected, and the false alarm probability of interference signals is reduced from 33% to 0.

Description

technical field [0001] The invention relates to an intelligent diagnosis technology of an aero-engine. It belongs to the field of fault diagnosis and health management. technical background [0002] At present, the analysis and diagnosis of aero-engine failures have the characteristics of intelligence. The degree of intelligence is reflected in the approximation of the nature of the problem between the mathematical model and the real system, as well as the artificial intelligence reproduction of domain expert knowledge. It is a comprehensive embodiment of artificial intelligence, other related disciplines and technologies in the fault diagnosis discipline. Combining a variety of different intelligent technologies to establish a reasonable hybrid reasoning model can effectively improve the intelligence of aeroengine fault diagnosis. [0003] The faults of each type of aeroengine are complex and diverse, but the typical faults can be exhaustive. There are many factors affec...

AI Technical Summary

Problems solved by technology

[0011] 1. The pattern recognition method has too many input dimensions, and there is a disaster of dimensionality, which makes the learning range too large and the pattern recognition time is long

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