Small-sample prediction validation testing: uncertainty-aware design and robust maintenance strategy for power electronic converters

Reliable degradation prediction is paramount for ensuring operational stability and minimizing failure risks in power electronic converters. Effective maintenance strategies, and thus system longevity and safety, depend on the accuracy of these predictions. However, verifying prediction accuracy with limited data and test durations presents a persistent challenge. This paper introduces a test design methodology for degradation prediction verification in power electronic converters, coupled with a preventive maintenance strategy that explicitly incorporates prediction uncertainties. Our approach provides a systematic means of determining the minimum test duration and a principled basis for selecting small sample sizes, enabling the derivation of robust maintenance plans that account for all potential prediction error scenarios. The methodology is validated through a case study of a three-phase inverter, demonstrating its efficacy with sample sizes of fewer than ten. The results highlight the practicality and effectiveness of the proposed methodology in assessing degradation prediction accuracy and, critically, illustrate the superior performance of the uncertainty-aware maintenance strategy in mitigating the risk of catastrophic failures.

Recommended citation: Sun, Q., Chen, C., Cai, X., Gao, J., Ye, X., Zhai, G., & Xie, M. (2025). Small-sample prediction validation testing: Uncertainty-aware design and robust maintenance strategy for power electronic converters. IEEE Transactions on Power Electronics, doi: https://doi.org/10.1109/TPEL.2025.3587316.
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