Journal of Korean Society of Industrial and Systems Engineering (산업경영시스템학회지)

Society of Korea Industrial and System Engineering (한국산업경영시스템학회)
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Fault Pattern Extraction Via Adjustable Time Segmentation Considering Inflection Points of Sensor Signals for Aircraft Engine Monitoring

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

  • Baek, Sujeong (Department of Industrial Management Engineering, Hanbat National University)
  • 백수정 (한밭대학교 산업경영공학과)
  • Received : 2021.08.06
  • Accepted : 2021.09.15
  • Published : 2021.09.30
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Abstract

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.

Keywords

Acknowledgement

This work was supported by the National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIT) (No. NRF-2019R1G1A1097478), This work was also supported by project for Industry-Academic Cooperation Based Platform R&D funded Korea Ministry of SMEs and Startups in 2020. (Project No. S3025825)

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