Prognostic by Classification of Predictions Combining Similarity-Based Estimation and Belief Functions
Forecasting the future states of a complex system is of paramount importance in many industrial applications covered in the community of Prognostics and Health Management (PHM). Practically, states can be either continuous (the value of a signal) or discrete (functioning modes). For each case, specific techniques exist. In this paper, we propose an approach called EVIPRO-KNN based on case-based reasoning and belief functions that jointly estimates the future values of the continuous signal and of the future discrete modes. A real datasets is used in order to assess the performance in estimating future break-down of a real system where the combination of both strategies provide the best prediction accuracies, up to 90%.
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