Case Study: a leading European engineering company improves predictive maintenance with Fractal's real-time sensor anomaly detection

An engineering company used gearbox sensor data to predict machine failures at an early stage

A leading European engineering company aimed to improve its maintenance efficiency by developing an algorithmic workflow for the automatic detection of gearbox anomalies using real-time sensor data from its turbines. The key challenges included integrating multiple sensor streams, predicting machine failures at an early stage, and deploying a solution capable of handling high-velocity data. To address this, they partnered with Fractal.

Fractal implemented a solution that analyzed sensor data using a dual-model approach to categorize trends, filter false alarms, and provide score-based anomaly detection. The system enabled real-time integration, triggered proactive maintenance alerts, and featured continuous model refinement. This resulted in significant maintenance cost savings, streamlined operations, and scalable, real-time predictive insights for the client. Fractal delivered a system with adaptive learning for ongoing improvement in detection precision.