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http://dx.doi.org/10.11627/jkise.2021.44.3.086

Fault Pattern Extraction Via Adjustable Time Segmentation Considering Inflection Points of Sensor Signals for Aircraft Engine Monitoring  

Baek, Sujeong (Department of Industrial Management Engineering, Hanbat National University)
Publication Information
Journal of Korean Society of Industrial and Systems Engineering / v.44, no.3, 2021 , pp. 86-97 More about this Journal
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
Fault Detection; Time Segmentation; Signal Discretization; Inflection Point; Sensor Data;
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