Evidential evolving gustafson-kessel algorithm for online data streams partitioning using belief function theory

Abstract — 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. I