research-article

Concept Drift Adaption for Online Anomaly Detection in Structural Health Monitoring

Authors:

Nguyen Lu Dang Khoa,

Fang ChenAuthors Info & Claims

CIKM '19: Proceedings of the 28th ACM International Conference on Information and Knowledge Management

Pages 2813 - 2821

https://doi.org/10.1145/3357384.3357816

Published: 03 November 2019 Publication History

Abstract

Despite its success for anomaly detection in the scenario where only data representing normal behavior are available, one-class support vector machine (OCSVM) still has challenge in dealing with non-stationary data stream, where the underlying distributions of data are time-varying. Existing OCSVM-based online learning methods incrementally update the model to address the challenge, however, they solely rely on the location relationship between a test sample and error support vectors. To better accommodate normal behavior evolution, online anomaly detection in non-stationary data stream is formulated as a concept drift adaptation problem in this paper. It is proposed that OCSVM-based incremental learning is only performed in the case of a normal drift. For an incoming sample, its relative relationship with three sets of vectors in OCSVM, namely margin support vectors, error support vectors, and reserve vectors is fully utilized to estimate whether a normal drift is emerging. Extensive experiments in the field of structural health monitoring have been conducted and the results have shown that the proposed simple approach outperforms the existing OCSVM-based online learning algorithms for anomaly detection.

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Concept Drift Adaption for Online Anomaly Detection in Structural Health Monitoring

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cover image ACM Conferences

CIKM '19: Proceedings of the 28th ACM International Conference on Information and Knowledge Management

November 2019

3373 pages

ISBN:9781450369763

DOI:10.1145/3357384

General Chairs:
Wenwu Zhu
Tsinghua University, China
,
Dacheng Tao
University of Massachusetts, USA
,
Xueqi Cheng
Institute of Computing Technology, CAS, China
,
Program Chairs:
Peng Cui
Tsinghua University, China
,
Elke Rundensteiner
Worcester Polytechnic Institute, USA
,
David Carmel
Amazon Research, USA
,
Qi He
LinkedIn, USA
,
Jeffrey Xu Yu
Chinese University of Hong Kong, China

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Published: 03 November 2019

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Cited By

Silva RJunior SZarpelão BDe Melo L(2024)Unsupervised Tuning for Drift Detectors Using Change Detector SegmentationIEEE Access10.1109/ACCESS.2024.338852912(54256-54271)Online publication date: 2024
https://doi.org/10.1109/ACCESS.2024.3388529
Stucchi DMagri LCarrera DBoracchi G(2023)Multimodal Batch-Wise Change DetectionIEEE Transactions on Neural Networks and Learning Systems10.1109/TNNLS.2023.329484634:10(6783-6797)Online publication date: Oct-2023
https://doi.org/10.1109/TNNLS.2023.3294846
Siddiqi SQureshi FLindstaedt SKern R(2023)Detecting Outliers in Non-IID Data: A Systematic Literature ReviewIEEE Access10.1109/ACCESS.2023.329409611(70333-70352)Online publication date: 2023
https://doi.org/10.1109/ACCESS.2023.3294096
Li HZheng WTang FZhu YHuang J(2023)Few-Shot Time-Series Anomaly Detection with Unsupervised Domain AdaptationInformation Sciences10.1016/j.ins.2023.119610(119610)Online publication date: Oct-2023
https://doi.org/10.1016/j.ins.2023.119610
Le-Nguyen MTurgis FFayemi PBifet A(2023)Exploring the potentials of online machine learning for predictive maintenance: a case study in the railway industryApplied Intelligence10.1007/s10489-023-05092-453:24(29758-29780)Online publication date: 1-Dec-2023
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Shu XZhang SLi YChen M(2022)An anomaly detection method based on random convolutional kernel and isolation forest for equipment state monitoringEksploatacja i Niezawodność – Maintenance and Reliability10.17531/ein.2022.4.1624:4(758-770)Online publication date: 16-Oct-2022
https://doi.org/10.17531/ein.2022.4.16
Le-Nguyen MTurgis FFayemi PBifet A(2022)Continuous Health Monitoring of Machinery using Online Clustering on Unlabeled Data Streams2022 IEEE International Conference on Big Data (Big Data)10.1109/BigData55660.2022.10021002(1866-1873)Online publication date: 17-Dec-2022
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Hossein Zadeh Bazargani MPakrashi ANamee B(2022)The Deep Radial Basis Function Data Descriptor (D-RBFDD) Network: A One-Class Neural Network for Anomaly DetectionIEEE Access10.1109/ACCESS.2022.318796110(70645-70661)Online publication date: 2022
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Martins IResende JSousa PSilva SAntunes LGama J(2022)Host-based IDS: A review and open issues of an anomaly detection system in IoTFuture Generation Computer Systems10.1016/j.future.2022.03.001133(95-113)Online publication date: Aug-2022
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