PoFINN: Integrating Physics of Failure and Neural Networks for Long-Term Degradation Forecasting of Aluminum Electrolytic Capacitors

Aluminum electrolytic capacitors play a critical role in power electronics, where degradation under storage conditions poses reliability challenges for long-lifecycle systems in defense, avionics, and industrial applications. This research introduces the physics-of-failure-informed neural network (PoFINN), which integrates electrolyte evaporation physics with neural architectures for accurate degradation forecasting. Existing approaches struggle with limited data, nonstationary patterns, and physically implausible predictions. PoFINN addresses these limitations through two key innovations: an embedded first-principles evaporation model capturing capacitance loss through mass balance equations and a dual-phase training methodology. This approach initializes long short-term memory weights using simulated physical data, and then refines parameters with empirical measurements. A parametric hybrid loss function balances physical constraints with data errors, maintaining plausibility while optimizing accuracy. Experimental validation shows PoFINN outperforms conventional forecasting approaches, achieving error reductions of 27%–81% across all time horizons compared to statistical, neural, and ensemble techniques. The most substantial improvements (70%–80%) are observed against recurrent architectures, with advantages more pronounced in long-term forecasting. Performance remains robust across different train–test splits (50–50, 60–40, and 70–30), highlighting PoFINN’s effectiveness with limited data—addressing a critical challenge in degradation prediction. The methodology shows immediate practical value for inventory management in reliability-critical sectors and potential applicability to other components, improving maintenance planning and replacement decisions.