Detecting an Intermittent Change of Unknown Duration

TL;DR

This paper reviews methods for detecting intermittent changes of unknown duration in processes, proposing a new criterion focused on local false alarm control and detection probability, with ML-based detection rules and simulation validation.

Contribution

It introduces a novel optimization criterion suited for intermittent change detection and shows that common detection rules are equivalent to ML-based stopping times.

Findings

Proposes a new criterion focusing on local false alarm probability.

Shows equivalence of CUSUM, window-limited CUSUM, and FMA to ML-based rules.

Provides simulation results validating the proposed approach.

Abstract

Oftentimes in practice, the observed process changes statistical properties at an unknown point in time and the duration of a change is substantially finite, in which case one says that the change is intermittent or transient. We provide an overview of existing approaches for intermittent change detection and advocate in favor of a particular setting driven by the intermittent nature of the change. We propose a novel optimization criterion that is more appropriate for many applied areas such as the detection of threats in physical-computer systems, near-Earth space informatics, epidemiology, pharmacokinetics, etc. We argue that controlling the local conditional probability of a false alarm, rather than the familiar average run length to a false alarm, and maximizing the local conditional probability of detection is a more reasonable approach versus a traditional quickest change detection approach that requires minimizing the expected delay to detection. We adopt the maximum likelihood (ML) approach with respect to the change duration and show that several commonly used detection rules (CUSUM, window-limited CUSUM, and FMA) are equivalent to the ML-based stopping times. We discuss how to choose design parameters for these rules and provide a comprehensive simulation study to corroborate intuitive expectations.