Prognostics-Based Identification of the Top-k
TITLE: Prognostics-Based Identification of the Top-k Units of a Fleet
SPEAKER: Dr. Nagi Gebraeel
This presentation considers a fleet of identical units (a fleet of aircrafts, trucks, etc.) where each unit in the fleet consists of several key components that are critical to the operation of a unit. We assume that each of these components degrades gracefully over time and its degradation process can be modeled using parameterized stochastic degradation models. The presentation discusses a novel approach that identifies the top-k, the k units with the largest residual life (most reliable), using sensor- based prognostic information associated with the critical components of each unit. We propose a Prognostics-Based Ranking Algorithm (PBR-Algorithm) that combines prognostics-based stochastic degradation models with computer science database ranking algorithms. The stochastic degradation models are used to compute residual life distributions (RLDs) of partially degraded critical components using condition monitoring data, whereas the ranking algorithm orders the units using the critical component as a ranking predicate and the Median of the RLD as a scoring criterion. The stochastic models used in this work utilize in-situ
degradation signals to continuously update the RLDs, in real-time. This creates a set of dynamically evolving RLDs. The non-parametric nature of the RLDs coupled with the need to perform real-time computations pose significant challenges in identifying the subset of k units with the largest remaining useful life. In this presentation, we will show that given the time stamp and amplitude/level of a component's degradation signal, it is possible to establish stochastic dominance among the RLDs of all the units in the fleet (for a given component type). We also
demonstrate the integration of the stochastic ordering results with advanced ranking algorithm-used for ranking of database queries in computerized search engines-for identifying the top-k units.