2025 - AVEVA World - San Francisco - Process Industries (Chemicals, MMM, Pulp/Paper)
ÇOLAKOGLU Metalurji A.S.: Predictive Maintenance for Industrial Cranes - Implementing Event-Driven Analytics to Reduce Downtime
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.
Industry
Mining Metals and Minerals
Company
Colakoglu Metalurji
Speaker
Özgür Özsoy
I have been working in various roles in the field of production operations for nearly 25 years. I completed my undergraduate studies in the Department of Metallurgical Engineering. Since joining Çolakoglu Metalurji in 2007, I have been serving as the Operations Director since 2015.
Session Code
SESS-214
