Fault detection of aircraft engine components using fuzzy unordered rule induction algorithm
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 nondestru...
محفوظ في:
| المؤلفون الرئيسيون: | , |
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| التنسيق: | Conference or Workshop Item |
| اللغة: | English English English |
| منشور في: |
2016
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| الموضوعات: | |
| الوصول للمادة أونلاين: | http://irep.iium.edu.my/54867/1/Fault%20Detection%20of%20Aircraft%20engine%20using%20fuzzy%20unordered%20induction%20system.pdf http://irep.iium.edu.my/54867/2/Programme_schedule.pdf http://irep.iium.edu.my/54867/3/List_of_publiction_with_ID_title.pdf http://irep.iium.edu.my/54867/ |
| الوسوم: |
إضافة وسم
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| المؤسسة: | Universiti Islam Antarabangsa Malaysia |
| اللغة: | English English English |
| الملخص: | 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. |
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