自适应部分观测序列变化检测与隔离
Adaptive Partially-Observed Sequential Change Detection and Isolation
摘要：
由于传感器在现代工业应用中的可访问性，高维数据已经变得流行。然而，一个具体的挑战是，由于有限的感测功率和资源约束，通常不容易获得完整的测量结果。此外，不同的故障模式可能存在于系统中，它是必要的，以确定真正的故障模式。这项工作的重点是在线自适应监测的高维数据在资源受限的环境中有多种潜在的故障模式。为了实现这一点，我们建议应用Shiryaev-Roberts程序的故障模式的水平，并利用多臂强盗来平衡的勘探和开采。我们进一步讨论了所提出的算法的理论性质，表明所提出的方法可以正确地隔离故障模式。最后，大量的仿真和两个案例研究表明，变点检测性能和故障模式隔离的准确性可以大大提高。
英文摘要：
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.
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