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.

1. 서 론
2. 관련 연구
    2.1 센서 데이터 기반 고장 감지 연구
    2.2 데이터 이산화 기법에서의 Time Segmentation
3. 센서 데이터의 변곡점을 고려한 고장 패턴추출 기법
    3.1 센서 데이터 이산화 기법을 통한 고장 패턴추출
    3.2 센서 데이터변곡점을 고려한 Adjustable TimeSegmentation
4. 항공기 엔진의 고장 패턴 추출 결과
    4.1 항공기 엔진 데이터
    4.2 Adjustable Time Segmentation을 통한 고장패턴 추출 결과
5. 결 론
References

• Sujeong Baek(한밭대학교 산업경영공학과) | 백수정 Corresponding Author