센서 데이터 변곡점에 따른 Time Segmentation 기반 항공기 엔진의 고장 패턴 추출

• 영문명: Fault Pattern Extraction Via Adjustable Time Segmentation Considering Inflection Points of Sensor Signals for Aircraft Engine Monitoring
• 발행기관: 한국산업경영시스템학회
• 저자명: 백수정(Sujeong Baek)
• 간행물 정보: 『산업경영시스템학회지』제44권 제3호, 86~97쪽, 전체 12쪽
• 주제분류: 공학 > 산업공학
• 파일형태: PDF
• 발행일자: 2021.09.30

영문 초록

As mechatronic systems have various, complex functions and require high performance, automatic fault detection is necessary for secure operation in manufacturing processes. For conducting automatic and real-time fault detection in modern mechatronic systems, multiple sensor signals are collected by internet of things technologies. Since traditional statistical control charts or machine learning approaches show significant results with unified and solid density models under normal operating states but they have limitations with scattered signal models under normal states, many pattern extraction and matching approaches have been paid attention. Signal discretization-based pattern extraction methods are one of popular signal analyses, which reduce the size of the given datasets as much as possible as well as highlight significant and inherent signal behaviors. Since general pattern extraction methods are usually conducted with a fixed size of time segmentation, they can easily cut off significant behaviors, and consequently the performance of the extracted fault patterns will be reduced. In this regard, adjustable time segmentation is proposed to extract much meaningful fault patterns in multiple sensor signals. By considering inflection points of signals, we determine the optimal cut-points of time segments in each sensor signal. In addition, to clarify the inflection points, we apply Savitzky-golay filter to the original datasets. To validate and verify the performance of the proposed segmentation, the dataset collected from an aircraft engine (provided by NASA prognostics center) is used to fault pattern extraction. As a result, the proposed adjustable time segmentation shows better performance in fault pattern extraction.

목차

1. 서론

2. 관련 연구

3. 센서 데이터의 변곡점을 고려한 고장 패턴 추출 기법

4. 항공기 엔진의 고장 패턴 추출 결과

5. 결론

키워드

Fault Detection Time Segmentation Signal Discretization Inflection Point Sensor Data