Efficient visibility algorithm for high-frequency time-series: application to fault diagnosis with graph convolutional network

Time series is a popular data type that is collected from various machines for fault diagnosis. Although most time-series models for fault diagnosis reflect local relations well, they cannot extract the global patterns that contain valuable information that can be used to recognize faults. To reflec...

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Published in:	Annals of operations research Vol. 339; no. 1-2; pp. 813 - 833
Main Authors:	Lee, Sangho, Choi, Jeongsub, Son, Youngdoo
Format:	Journal Article
Language:	English
Published:	New York Springer US 01.08.2024 Springer Nature B.V
Subjects:	Algorithms Artificial neural networks Business and Management Combinatorics Computational efficiency Computing costs Cost analysis Data points Fault diagnosis Graphs Operations Research/Decision Theory Original Research Pattern recognition Rotating machinery Theory of Computation Time series Visibility Deep learning Fault diagnosis High-frequency time series Graph convolutional network Visibility algorithms
ISSN:	0254-5330, 1572-9338
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Abstract	Time series is a popular data type that is collected from various machines for fault diagnosis. Although most time-series models for fault diagnosis reflect local relations well, they cannot extract the global patterns that contain valuable information that can be used to recognize faults. To reflect the global structural information of a time series, many recent studies have used a graph constructed by visibility algorithms (VAs) that convert a time series into a graph. However, applying the VAs to high-frequency time series—which the machines typically generate—is challenging because the computational burden of the VAs increases with the length of a time series. Therefore, we propose a novel graph-based fault diagnosis framework for high-frequency time series. First, we propose an efficient VA (EVA) that extracts essential data points to characterize a time series and constructs a graph from a high-frequency time series. Not only do the EVAs convert a given time series faster into a graph than the VAs, but the resulting graphs also characterize the time-series structure with simplicity and clarity by selecting essential data points. Then, we adopt a graph convolutional network to analyze the resulting graphs and diagnose faults. We verified the characteristics of the EVAs and the fault diagnosis performance of the proposed framework using toy time series and public rotating machinery datasets, respectively. The results demonstrated that, compared to the VAs, the EVAs are efficient in terms of computational cost, and the proposed framework is effective for fault diagnosis.
AbstractList	Time series is a popular data type that is collected from various machines for fault diagnosis. Although most time-series models for fault diagnosis reflect local relations well, they cannot extract the global patterns that contain valuable information that can be used to recognize faults. To reflect the global structural information of a time series, many recent studies have used a graph constructed by visibility algorithms (VAs) that convert a time series into a graph. However, applying the VAs to high-frequency time series—which the machines typically generate—is challenging because the computational burden of the VAs increases with the length of a time series. Therefore, we propose a novel graph-based fault diagnosis framework for high-frequency time series. First, we propose an efficient VA (EVA) that extracts essential data points to characterize a time series and constructs a graph from a high-frequency time series. Not only do the EVAs convert a given time series faster into a graph than the VAs, but the resulting graphs also characterize the time-series structure with simplicity and clarity by selecting essential data points. Then, we adopt a graph convolutional network to analyze the resulting graphs and diagnose faults. We verified the characteristics of the EVAs and the fault diagnosis performance of the proposed framework using toy time series and public rotating machinery datasets, respectively. The results demonstrated that, compared to the VAs, the EVAs are efficient in terms of computational cost, and the proposed framework is effective for fault diagnosis.
Author	Lee, Sangho Choi, Jeongsub Son, Youngdoo
Author_xml	– sequence: 1 givenname: Sangho orcidid: 0000-0002-7784-8515 surname: Lee fullname: Lee, Sangho organization: Department of Industrial and Systems Engineering, Dongguk University – Seoul, Data Science Laboratory (DSLAB), Dongguk University – Seoul – sequence: 2 givenname: Jeongsub orcidid: 0000-0003-2220-295X surname: Choi fullname: Choi, Jeongsub organization: Department of Management Information Systems, West Virginia University – sequence: 3 givenname: Youngdoo orcidid: 0000-0002-1912-5853 surname: Son fullname: Son, Youngdoo email: youngdoo@dongguk.edu organization: Department of Industrial and Systems Engineering, Dongguk University – Seoul, Data Science Laboratory (DSLAB), Dongguk University – Seoul
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Snippet	Time series is a popular data type that is collected from various machines for fault diagnosis. Although most time-series models for fault diagnosis reflect...
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SubjectTerms	Algorithms Artificial neural networks Business and Management Combinatorics Computational efficiency Computing costs Cost analysis Data points Fault diagnosis Graphs Operations Research/Decision Theory Original Research Pattern recognition Rotating machinery Theory of Computation Time series Visibility
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Title	Efficient visibility algorithm for high-frequency time-series: application to fault diagnosis with graph convolutional network
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