SENTRYPdM: A Unified Alarm Governance Architecture for False-Alarm-Resilient Predictive Maintenance

Abstract

False alarms remain a primary obstacle to the industrial scalability of predictive maintenance (PdM) deployment. While existing research focuses on refining Remaining Useful Life (RUL) prediction accuracy, relatively little attention has been paid to the systemic governance required to distinguish true degradation from exogenous disturbances, sensor drifts, or transient spikes. This paper proposes SENTRYPdM, a unified and testable architecture designed to augment standard PdM pipelines with principled alarm governance. The architecture integrates several core mechanisms, including: (i) signal hygiene and exogenous debiasing, which use interventionaware forecasting to filter operational offsets; (ii) crosshorizon agreement (CHA) to enforce RUL consistency across multiple look-ahead windows; (iii) residual changepoint diagnostics to differentiate structural failures from reversible excursions; (iv) surge disambiguation for catastrophic event detection using extreme-value tails; and (v) adaptive decision logic (ADL++) to synthesize multimodal evidence into cost-optimized alerts. Validation using BOSCH DRIE production data and a stress test across twelve diverse failure scenarios (Cases A-L) demonstrates that SENTRYPdM significantly reduces the mean Beginning-to-End (BTE) error from 1.686 to 0.239, without sacrificing early-warning lead time. The results indicate that the proposed framework can function as a “plug-and-play” layer to enhance operator trust and operational reliability in smart manufacturing environments.