Description
Predictive maintenance relies on clustering algorithms to track unwanted changes in industrial processes and infrastructures. However, most of them tend to fail to detect slow deviations indicating a gradual deterioration of the system. Our solution: Dyclee, a clustering algorithm developed at LAAS-CNRS offers remarkable capabilities due to its ability to dynamically track smooth and/or abrupt changes in evolving conditions.Applications
- Predictive maintenance
- System health diagnosis
- Process monitoring
- Data analytics
