Flexible Reliability Assessment of Electronic Components Under Complex Failure-Mode Scenarios

One of the most important aspects of reliability engineering is modeling the failure behavior of devices, which can exhibit monotonic or non-monotonic patterns and depend explicitly on design, internal components, and operating conditions, often leading to multiple failure modes. Methodologies exist to address these scenarios; however, many lack the flexibility to capture diverse behaviors throughout the device’s lifespan. Given this, this paper presents a novel reliability model based on the Competitive Risk (CR) framework. This model comprises a minimal variable derived from the Perks Risk Model Type I (PRMTI) to capture risk-rate patterns of different shapes. Unlike existing CR and additive models, the proposed approach effectively handles failure-time data with increasing, bathtub, inverted, or modified risk rates. Relevant mathematical properties for reliability scenarios are presented and analyzed. Furthermore, two approaches for parameter estimation are offered: the maximum likelihood method (MLE) and Bayesian inference (BaI) using the Hamiltonian Monte Carlo (HMC) method. Finally, to verify the proposed methodology, two case studies and a comparative analysis are presented, in which the PRMTI is tested against six other methodologies with similar properties. The results show that the PRMTI outperforms empirical calculations, offering greater agreement and more accurate predictions of failure probabilities. These findings highlight the model’s versatility and accuracy in representing complex failure mechanisms in reliability studies.