Particle Filter-Based Prognostics for an Electrolytic Capacitor Working in Variable Operating Conditions

Abstract : Prognostic models should properly take into account the effects of operating conditions on the degradation process and on the signal measurements used for monitoring. In this work, we develop a Particle Filter-based (PF) prognostic model for the estimation of the Remaining Useful Life (RUL) of aluminum electrolytic capacitors used in electrical automotive drives, whose operation is characterized by continuously varying conditions. The capacitor degradation process, which remarkably depends from the temperature experienced by the component, is typically monitored by observing the capacitor Equivalent Series Resistance (ESR). However, the ESR measurement is influenced by the temperature at which the measurement is performed, which changes depending on the operating conditions. To address this problem, we introduce a novel degradation indicator independent from the measurement temperature. Such indicator can, then, be used for the prediction of the capacitor degradation and its RUL. For this, we develop a Particle Filter prognostic model, whose performance is verified on data collected in simulated and experimental degradation tests.
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Probabilistic Engineering Mechanics, Elsevier, 2015, pp.13
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  • HAL Id : hal-01177011, version 1

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Marco Rigamonti, Piero Baraldi, Enrico Zio, Daniel Astigarraga, Ainhoa Galarza. Particle Filter-Based Prognostics for an Electrolytic Capacitor Working in Variable Operating Conditions. Probabilistic Engineering Mechanics, Elsevier, 2015, pp.13. 〈hal-01177011〉

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