Adaptive Partially Observed Sequential Change Detection and Isolation. Issue 4 (2nd October 2022)

• Record Type: Journal Article
• Authors: Zhao, Xinyu; Hu, Jiuyun; Mei, Yajun; Yan1, Hao
• Is Part Of: Technometrics. Volume 64:Issue 4(2022)
• Page Start: 502
• Page End: 512
• Publication Date: 2022-10-02
• Subjects: Adaptive sampling -- Multi-arm bandit -- Multiple failure modes -- Sequential change-point detection -- Shiryaev–Roberts procedure; Statistical physics -- Periodicals; Chemistry -- Statistical methods -- Periodicals; Engineering -- Statistical methods -- Periodicals; 519.5
• DOI: 10.1080/00401706.2022.2124307
• Languages: English
• ISSNs: 0040-1706

Abstract

ABSTRACT: High-dimensional data has become popular due to the easy accessibility of sensors in modern industrial applications. However, one specific challenge is that it is often not easy to obtain complete measurements due to limited sensing powers and resource constraints. Furthermore, distinct failure patterns may exist in the systems, and it is necessary to identify the true failure pattern. This work focuses on the online adaptive monitoring of high-dimensional data in resource-constrained environments with multiple potential failure modes. To achieve this, we propose to apply the Shiryaev–Roberts procedure on the failure mode level and use the multi-arm bandit to balance the exploration and exploitation. We further discuss the theoretical property of the proposed algorithm to show that the proposed method can correctly isolate the failure mode. Finally, extensive simulations and two case studies demonstrate that the change point detection performance and the failure mode isolation accuracy can be greatly improved.