Fuzzy Model Optimization Using Genetic Algorithm for Aircraft Engine Diagnosis

An accurate and up-to-date diagnostic model is critical for economic aircraft engine operation. However, for many commercial airline fleets, monitoring and diagnosing engine faults is often left to human operators due to lack of effective modeling. Individuals must manually interpret engine performance parameters and this results in inconsistent evaluation and the potential for error. A recent work has been performed to capture the knowledge of diagnostic engineers and apply fuzzy logic to engine fault classification. The developed fuzzy model is capable of capturing various engine fault signatures based on expert experience, and it provides accurate diagnosis by assessing the similarity between those fault signatures and observed trends in sensor data. The major drawback with this approach is the manual knowledge extraction and model maintenance process. Manual model tuning is not only labor intensive, but it also brings another source of inconsistency to the overall system performan...

• Type: Conference
• Year: 2006
• Where: FLAIRS
• Authors: LiJie Yu, Daniel J. Cleary, Mark D. Osborn, Vrinda Rajiv
• Keywords: Artificial Intelligence, Diagnostic Model, Engine Fault, FLAIRS 2006, Up-to-date Diagnostic Model