Dyclee: Dynamic Clustering for tracking Evolving Environments


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.


  • Predictive maintenance
  • System health diagnosis
  • Process monitoring
  • Data analytics