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An angle-based subspace anomaly detection approach to high-dimensional data: With an application to industrial fault detection

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  • Zhang, Liangwei
  • Lin, Jing
  • Karim, Ramin

Abstract

The accuracy of traditional anomaly detection techniques implemented on full-dimensional spaces degrades significantly as dimensionality increases, thereby hampering many real-world applications. This work proposes an approach to selecting meaningful feature subspace and conducting anomaly detection in the corresponding subspace projection. The aim is to maintain the detection accuracy in high-dimensional circumstances. The suggested approach assesses the angle between all pairs of two lines for one specific anomaly candidate: the first line is connected by the relevant data point and the center of its adjacent points; the other line is one of the axis-parallel lines. Those dimensions which have a relatively small angle with the first line are then chosen to constitute the axis-parallel subspace for the candidate. Next, a normalized Mahalanobis distance is introduced to measure the local outlier-ness of an object in the subspace projection. To comprehensively compare the proposed algorithm with several existing anomaly detection techniques, we constructed artificial datasets with various high-dimensional settings and found the algorithm displayed superior accuracy. A further experiment on an industrial dataset demonstrated the applicability of the proposed algorithm in fault detection tasks and highlighted another of its merits, namely, to provide preliminary interpretation of abnormality through feature ordering in relevant subspaces.

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  • Zhang, Liangwei & Lin, Jing & Karim, Ramin, 2015. "An angle-based subspace anomaly detection approach to high-dimensional data: With an application to industrial fault detection," Reliability Engineering and System Safety, Elsevier, vol. 142(C), pages 482-497.
    • Handle: RePEc:eee:reensy:v:142:y:2015:i:c:p:482-497
    DOI: 10.1016/j.ress.2015.05.025
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    Cited by:

    1. Xiaoqiang Liu & Ji Li & Lei Shao & Hongli Liu & Lei Ren & Lihua Zhu, 2023. "Transformer Fault Early Warning Analysis Based on Hierarchical Clustering Combined with Decision Trees," Energies, MDPI, vol. 16(3), pages 1-14, January.
    2. Chen, Zhen & Zhou, Di & Zio, Enrico & Xia, Tangbin & Pan, Ershun, 2023. "Adaptive transfer learning for multimode process monitoring and unsupervised anomaly detection in steam turbines," Reliability Engineering and System Safety, Elsevier, vol. 234(C).
    3. Zhang, Liangwei & Lin, Jing & Shao, Haidong & Zhang, Zhicong & Yan, Xiaohui & Long, Jianyu, 2021. "End-to-end unsupervised fault detection using a flow-based model," Reliability Engineering and System Safety, Elsevier, vol. 215(C).
    4. Ota, Shuhei & Kimura, Mitsuhiro, 2017. "A statistical dependent failure detection method for n-component parallel systems," Reliability Engineering and System Safety, Elsevier, vol. 167(C), pages 376-382.
    5. Moghaddass, Ramin & Sheng, Shuangwen, 2019. "An anomaly detection framework for dynamic systems using a Bayesian hierarchical framework," Applied Energy, Elsevier, vol. 240(C), pages 561-582.

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