Reliability prediction for electrical - electronic devices: a comprehensive review of methods, challenges, and future directions

Reliability prediction is fundamental to electronic system design, lifetime estimation, risk assessment, and maintenance planning. Over the past decades, diverse prediction approaches have emerged, spanning empirical handbook-based methods, physics-of-failure, life testing, state-space modeling, data-driven techniques, and hybrid frameworks. However, increasing system complexity and operating variability have revealed key limitations of empirical standards such as MIL-HDBK-217F and Telcordia SR-332, especially in physical interpretability, lifecycle coverage, and adaptability to modern applications. To address these challenges, this paper reviews and compares six representative reliability prediction models, highlighting their theoretical basis as well as their strengths and weaknesses. Particular attention is given to recent data-driven and hybrid approaches that combine physical insight with statistical learning to enhance prediction accuracy and operational relevance. From a lifecycle perspective, the paper also identifies research gaps and outlines directions toward more scalable, interpretable, and practically applicable reliability prediction frameworks for contemporary electronic systems.