Browsing by Author "Orchard, Marcos E."

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    Expected First Occurrence Time of Uncertain Future Events in One-Dimensional Linear Systems
    (Prognostics and Health Management Society, 2024) Acuña Ureta, David Esteban; Fuentealba Secul, Diego Ignacio; Orchard, Marcos E.
    The rapid advancement of machine learning algorithms has significantly enhanced tools for monitoring system health, making data-driven approaches predominant in Prognostics and Health Management (PHM). In contrast, model-based approaches have seen modest progress, as they are often constrained by the need for prior knowledge of specific governing equations, limiting their applicability to a wide range of problems. Recently, rigorous theoretical foundations have been established to extend dynamical systems theory by incorporating prognosis of uncertain events. This article leverages this formal framework to introduce and demonstrate a fundamental mathematical result for one-dimensional linear dynamical systems. The presented theorem offers an analytical expression for approximating the expected time at which an event will first occur in the future. Unlike typical thresholds, this event is triggered by a hazard zone, defined as an uncertain event likelihood function over the system’s state space. Applications of this theorem can be found in implementing real-time prognostic frameworks, where it is crucial to quickly estimate the magnitude of impending failures. Emphasis is placed on minimizing computational burden to facilitate prognostic decision-making.
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    GNSS-Based Estimation of Average Instantaneous Power Consumption in Electric Vehicles
    (2023) Bustos, Javier A. Torres; Orchard, Marcos E.; Torres-Torriti, Miguel; Cheein, Fernando Auat
    Several countries are currently leading the challenge of replacing internal combustion engines (ICE) in vehicles by electrically powered ones, mainly motivated by the goal of reducing the dependence on oil and the carbon footprint. However, the autonomy of electric vehicles (EVs) remains significantly lower than their ICE counterpart: it depends on the amount of energy stored and its rate of utilization, characterized by the instantaneous power consumption (IPC). Although IPC can be measured from on-board voltage and current sensors, in route planning applications it is of paramount importance to generate prior estimates from other sources of information, such as the mass of the vehicle, its velocity, acceleration, aerodynamic, and rolling resistances (among other vehicle and terrain parameters). In this work, we propose the estimation of average IPC for a given path, using only localization information provided by a global navigation satellite system antenna, at different levels of errors. Our work is experimentally validated with an EV on the road, achieving on average a 99.7% of power estimation accuracy for a localization error of +/- 1.5 m-as best case-and 73.6% accuracy for +/- 8.6 m of localization error-as worst case. These results encourages us to use only position data to estimate average IPC of EVs, avoiding the need of placing extra sensors into the vehicles.