Evidential Evolving Gustafson-Kessel Algorithm (E2GK) and its application to PRONOSTIA's Data Streams Partitioning.

Lisa Serir (1) , Emmanuel Ramasso (1) , Patrick Nectoux (1) , Olivier Bauer (1) , Noureddine Zerhouni (1)

1 FEMTO-ST - Franche-Comté Électronique Mécanique, Thermique et Optique - Sciences et Technologies (UMR 6174)

Condition-based maintenance (CBM) appears to be a key element in modern maintenance practice. Research in diagnosis and prognosis, two important aspects of a CBM program, is growing rapidly and many studies are conducted in research laboratories to develop models, algorithms and technologies for data processing. In this context, we present a new evolving clustering algorithm developed for prognostics perspectives. E2GK (Evidential Evolving Gustafson-Kessel) is an online clustering method in the theoretical framework of belief functions. The algorithm enables an online partitioning of data streams based on two existing and efficient algorithms: Evidantial c-Means (ECM) and Evolving Gustafson-Kessel (EGK). To validate and illustrate the results of E2GK, we use a dataset provided by an original platform called PRONOSTIA dedicated to prognostics applications.

Domaines: Automatique / Robotique

Lisa Serir, Emmanuel Ramasso, Patrick Nectoux, Olivier Bauer, Noureddine Zerhouni. Evidential Evolving Gustafson-Kessel Algorithm (E2GK) and its application to PRONOSTIA's Data Streams Partitioning.. 50th IEEE Conference on Decision and Control and European Control Conference, CDC-ECC'12., Dec 2011, Orlando, Floride, United States. pp.8273-8278. ⟨hal-00720008⟩