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논문 기본 정보
- 자료유형
- 학술저널
- 저자정보
- 발행연도
- 2021.9
- 수록면
- 86 - 97 (12page)
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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.
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참고문헌 (34)
참고문헌 신청
Ahn, B. / 2021 / A Study on the Fault Diagnosis of Wind Turbines Using SVM Based on Cross Validation / Proceedings of 2021 Spring Conference of The Korean Institute of Electrical Engineers :136~137
Baek, S. / 2019 / Abrupt Variance and Discernibility Analyses of Multi-Sensor Signals for Fault Pattern Extraction / Computers & Industrial Engineering 128:999~1007
Baek, S. / 2017 / Empirical Sensitivity Analysis of Discretization Parameters for Fault Pattern Extraction from Multivariate Time Series Data / IEEE Transactions on Cybernetics 47(5):1198~1209
Bai, X / 2013 / Co-anomaly Event Detection in Multiple Temperature Series / Proceedings of the 6th International Conference of Knowledge Science, Engineering, and Management :1~14
Chandola, V. / 2012 / Anomaly Detection for Discrete Sequences: A Survey / IEEE Transactions on Knowledge and Data Engineering 24(5):823~839
Baek, S. / 2019 / Abrupt Variance and Discernibility Analyses of Multi-Sensor Signals for Fault Pattern Extraction / Computers & Industrial Engineering 128:999~1007
Baek, S. / 2017 / Empirical Sensitivity Analysis of Discretization Parameters for Fault Pattern Extraction from Multivariate Time Series Data / IEEE Transactions on Cybernetics 47(5):1198~1209
Bai, X / 2013 / Co-anomaly Event Detection in Multiple Temperature Series / Proceedings of the 6th International Conference of Knowledge Science, Engineering, and Management :1~14
Chandola, V. / 2012 / Anomaly Detection for Discrete Sequences: A Survey / IEEE Transactions on Knowledge and Data Engineering 24(5):823~839
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