Challenge
The company faced the challenge of frequent unplanned machine failures, leading to significant production disruptions and high costs. Risks included production losses, increased maintenance costs, and potential damage to reputation due to delivery delays. A solution was necessary to ensure production continuity and optimize maintenance costs.
Approach
Our approach involved developing a predictive model for early anomaly detection. We utilized advanced data analytics and machine learning to identify patterns and anomalies in machine data. The solution was integrated into the customer’s existing monitoring system and tested offline to detect artificially induced anomalies. Additionally, automatic capturing, storage, and classification of detected anomalies were implemented.
Results
The developed anomaly detection model demonstrated near real-time capability and was successfully validated in live tests. Through implementation, scheduled maintenance could be conducted more efficiently, and unplanned downtime was significantly reduced. The customer benefited from increased production efficiency and substantial cost savings.