Application of the Korean Reliability Databook for Improving the Accuracy of Weapon System Reliability Prediction

Purpose: Reliability prediction during weapon system development requires failure rate data for individual components. Prediction models such as MIL-HDBK-217F and SR-332 are widely used but exhibit several limitations. As an alternative, failure rate databooks derived from field failure data have gained attention. This study investigated the applicability of the Korean Reliability Databook (KRDB) to enhance the accuracy of reliability prediction for weapon systems.<br/>Methods: This study reviewed current reliability prediction methods for weapon systems and identified their limitations. It then conducted qualitative and quantitative comparisons between the KRDB and several foreign databooks, including Off shore and On shore Reliability Data (OREDA), the Weibull Analysis Handbook, Generic Component Reliability Data(GRCD), Electronic Parts Reliability Data(EPRD), and Non-electronic Parts Reliability Data(NPRD). Comparisons were performed at the component and assembly levels to assess the differences in predicted failure rates.<br/>Results: The KRDB, based on actual field operation data, more accurately reflected domestic operational environments. Failure rates derived at the assembly level using empirical data helped address the overestimation issues commonly observed in conventional methods. In contrast to conventional methods, which assume serial connections among components, the KRDB provided a more rational prediction by incorporating actual assembly-level failure data.<br/>Conclusion: With continuous updates and systematic component categorization, the KRDB is expected to expand its applicability. It can be effectively applied in reliability design, maintenance optimization, and Performance-Based Logistics based on RAM-C metrics.