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A Bidirectional LSTM-Based Prognostication of Electrolytic Capacitor
Progress In Electromagnetics Research C, Vol. 109, 139-152, 2021
Knowing the state-of-health (SOH) of equipment, device or component is very essential for the secure and dependable operation of a system. Electrolytic capacitors are undoubtedly one of the essential components of power supply modules used in aerial and underwater vehicles, and every equipment requires a conversion of voltage from one level to another. This has encouraged research into the components of the power supply used in such systems of which electrolytic capacitor is of interest in this study. In this paper, we explore a new approach to implementing prognostics and health management (PHM) for electrolytic capacitors and propose a method of estimating the SOH leading to the prediction of the remaining useful life (RUL). This is accomplished by using a bidirectional long short-term memory (BLSTM) network to capture the degradation trends. We demonstrate the power and leverage that this method brings to bear in encoding time-domain dependencies in accurately estimating the SOH bereft of state models as employed in traditional methods. We validate the proposed approach using capacitor data recorded at different electrical over-stress accelerated aging conditions. The proposed method surpasses other existing methods in RUL prediction as indicated by the error and relative accuracy.
Delanyo Kwame Bensah Kulevome, Hong Wang, and Xuegang Wang, "A Bidirectional LSTM-Based Prognostication of Electrolytic Capacitor," Progress In Electromagnetics Research C, Vol. 109, 139-152, 2021.

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