The dominant failure mode of industrial predictive maintenance is not model inaccuracy. It is a broken handoff between detection and response. This paper describes an integration architecture that connects ML-based anomaly detection to the plant's maintenance execution system, converting a monitoring dashboard into an action-producing system of record.
