An Evidential Evolving Prognostic Approach and its Application to PRONOSTIA’s Data Streams



Lisa Serir Emmanuel Ramasso Noureddine Zerhouni


The research activity in the PHM community is in full bloom and many efforts are being made to develop more realistic and reliable methodologies. However, there still exist very few real-world applications due to the complexity of the systems of interest. Nonlinear dynamical systems identification and behavior prediction are difficult problems encountered in prognosis. The difficulty in switching from theory to practice can partially be explained by the existence of different kinds of uncertainty at each step of the implementation that must be taken into account with the appropriate tools. In this paper, we propose an evolving multi-modeling approach for the detection, the adaptation and the combination of local models in order to analyze complex systems behavior. It relies on belief functions in order to take into consideration the uncertainty related to the available data describing the system as well as the uncertainty generated by the nonlinearity of the system. The information of doubt explicitly represented in the belief functions framework is exploited to properly segment the data and take into account the uncertainty related to the transitions between the operating regions. The proposed algorithm is validated on a data provided by PRONOSTIA platform.

How to Cite

Serir, L. ., Ramasso, E. ., & Zerhouni, N. . (2012). An Evidential Evolving Prognostic Approach and its Application to PRONOSTIA’s Data Streams. Annual Conference of the PHM Society, 4(1).
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Online evidential clustering, Multi-modeling, Belief functions theory, Behavior modeling, Virtual centroids

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