Fault detection of aircraft engine components using fuzzy unordered rule induction algorithm

Le résumé fourni par la source

Nondestructive Fault monitoring is of paramount importance in safety and reliability of aircraft engine operations and timely maintenance of its critical components. There have been numerous attempts by researchers to tackle the problem of nondestructive fault monitoring. The bottleneck in nondestructive fault monitoring lies in data analysis. State-of-the-art systems are not accurate due to high dimensionality of sensory data. This paper proposes auto encoder neural network for compressing of high dimensional sensory data and classification using Fuzzy Unordered Rule Induction Algorithm (FURIA) with emphasis on detection and isolation of incipient faults. The preliminary results demonstrate the efficacy of the proposed system.

Ce résumé expose les affirmations des auteurs. BNTIC ne l’interprète pas comme une validation indépendante des résultats.