Abstract:

This paper investigates a specific operational amplifier to overcome the limitations of traditional accelerated degradation testing, particularly issues related to limited data availability and inaccurate modeling. A particle swarm optimization algorithm is employed to design an optimal testing strategy that achieves a balance between model precision, reliability evaluation confidence, and cost-effectiveness. Utilizing the degradation data derived from experimental procedures, a time-scale function is incorporated into the Wiener process to model the nonlinear degradation behavior of the operational amplifier, accounting for the substantial randomness in environmental influences and the uncertainty associated with actual usage conditions. Furthermore, this study proposes a multivariate performance degradation modeling approach utilizing Copula functions to address product failures arising from competing performance parameters. Compared to conventional multivariate normal models, the proposed approach offers distinct advantages in accurately capturing complex nonlinear dependencies among performance parameters, streamlining the modeling process, and improving both flexibility and applicability. In the presented case study, an optimized reliability testing procedure is applied to a specific operational amplifier, followed by degradation modeling and reliability evaluation utilizing the proposed methodology. The results demonstrate that this integrated approach achieves 14.2% cost reduction, providing engineers with a practical framework for designing cost-effective testing programs without compromising assessment accuracy. The proposed methodology not only advances degradation modeling techniques but also offers direct economic benefits for industrial reliability testing applications.

Key words: operational amplifier, accelerated degradation testing optimization, degradation modeling, Copula functions, Wiener stochastic process