Abstract

Electronics subsystems play an increasingly important role in safety critical systems for monitoring, control, and enhanced functionality. Electrolytic capacitors are an important component in ,amy subsystems that range from power supplies on safety critical avionics equipment to power drivers for electro-mechanical actuators. These capacitors are ideal forpassing or bypassing low-frequency signals in power supplies but are known to have lower reliability compared to ceramic and tantalum capacitors, and given their criticality in electronicssubsystems they are a good candidate for component level monitoring and prognostics. Prognostics provides a way to assess remaining useful life of components and systems based on their current state of health and their anticipated future use and operating conditions. Past experiences have shown that capacitor degradation and failures are quite prevalent under high electrical and thermal stress conditions that they experience during operations. Our focus in this work is on deriving a physics-based degradation model for electrolytic capacitors under thermal stress conditions. As part of our methodology, we study the effects of accelerated aging due to thermal stress on a batch of capacitors stored at high ambient temperature conditions. This provides a framework for supplementing theoretical modeling with data collected from simultaneous experiments, which is then used to validate the derived models. This work represents a first step toward combining data driven and physics-based approaches for modeling capacitor degradation. The case study shows how the proposed technique is applied to a batch of identically manufactured capacitors.

Highlights

  • Many devices and systems today consist of embedded electronic modules for monitoring, control, and enhanced functionality

  • The errors from the results of the predicted model and actual data are the summarized in Tables (2) and (3), and compared using the mean (X ), median absolute deviation (X ), root mean squared error (RMSE), root mean squared percentage error (RMSPE), standard deviation (SD), and confidence interval (CI) to study the overall predicted model behavior

  • Degradation model, D3 was validated against the computed change in volume of capacitor #4, and model, D1 was validated for decrease in capacitance

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Summary

Introduction

Many devices and systems today consist of embedded electronic modules for monitoring, control, and enhanced functionality. It has been found that these modules are often the first elements in the system to fail reducing overall system reliability (Vichare & Pecht, 2006; FIDESGroup, 2004) These failures can often be attributed to adverse storage and operating conditions, such as high temperatures, voltage surges and current spikes that the modules and their components may be subjected to during operation. This motivates the need for developing Integrated Vehicle Health Management (IVHM) technologies for systems with embedded electronics. An understanding of the deterioration mechanisms helps to systematically track the changes in system behavior and performance, develop the capability to anticipate failures, and predict the remaining useful life (RUL) of the electronic systems long before the components fail

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