Industrial crane systems are critical to material handling operations, yet their unpredictable and non-continuous usage patterns pose significant challenges for maintenance strategies. This study presents the successful implementation of AVEVA Predictive Analytics in a crane plant, highlighting the adaptation of a traditionally continuous monitoring system for intermittent operations.

The project leverages IoT sensor integration, event-driven data collection, and predictive modeling to optimize maintenance, reduce unplanned downtime, and enhance operational safety. A novel event-based evaluation strategy was developed to accommodate the irregular operating schedules of cranes. Instead of relying on standard threshold-based alerts, the system uses Locality Sensitive Hashing [LSH] for dynamic anomaly detection, identifying faults through real-time deviations from learned healthy patterns. Additionally, Overall Model Residual [OMR] analysis quantifies deviations, ensuring precise and timely maintenance interventions while minimizing false alarms.