Reducing Alarm Fatigue in Dam Pendulum Monitoring: Streak Detection of Prediction-Band Transgressions with a Two-State Hidden Markov Model

Dam-safety monitoring flags pendulum readings outside a prediction band, but autocorrelated residuals make these transgressions cluster into streaks of redundant warnings causing alarm fatigue. This study aimed to reduce it by learning the normal behaviour of the transgression series and warning only when their persistence departs from it. Three detectors are compared: a Gaussian-threshold baseline (M0), a sliding-window detector with a 99th-percentile threshold (P99), and a two-state hidden Markov model (HMM) that learns streak persistence. They were applied to 28 pendulums of three dams (1, 23, and 4 sensors), and an identifiability criterion, met by 20, decides when the HMM can be trusted. In Dam2, the most representative, the HMM reduces flagged records and episodes by 26.0% and 33.3% with respect to P99 among identifiable sensors and the proposed configuration (HMM where identifiable, P99 otherwise) by 19.2% and 25.8%, with variation across sensors. On a synthetic series with habitual streaks and a known drift, the HMM flagged only the drift, after 12 records, whereas the other detectors, including rules of two, three, and five consecutive transgressions, also flagged the habitual streaks. Operationally, the HMM replaces such hand-set rules with a persistence learned from each sensor’s data.