Abstract
The aim of this paper is to present a methodology for the assessment of prognosis quality and more specifically, the Remaining Useful Life prediction in maintenance applications. The utilization of model-driven physics-based approaches in combination with prognostics is a common practice to predict a machine’s future health status. Due to the increased utilization of this concept, a methodology to estimate the prediction error is required. Scope of the paper is to present a prediction assessment methodology. The proposed concept is also validated through a predictive maintenance application in robotics.
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