  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.
