Adaptive Partially-Observed Sequential Change Detection and Isolation

TL;DR

This paper introduces an adaptive, resource-efficient method for detecting and isolating failure modes in high-dimensional, partially observed data streams using a combination of Shiryaev-Roberts procedure and multi-arm bandit algorithms.

Contribution

It proposes a novel online adaptive framework that integrates change detection and failure mode isolation in resource-constrained high-dimensional systems.

Findings

Improved change point detection accuracy.

Enhanced failure mode isolation performance.

Theoretical guarantees for correct failure mode identification.

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 utilize 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.