Complex electronic systems have a structures that can lead to coupling failure mechanisms and difficulties in collecting measured data. These issues increase the difficulty of reliability analysis. Current reliability research methods cannot effectively solve the above problems. In this paper, we propose a new approach that combines a physics of failure (PoF) method and a copula Bayesian network to assess complex electronic systems. The proposed approach improves the defects of PoF methods and traditional Bayesian networks when applied to the reliability analysis of complex electronic systems. A copula Bayesian network is used to realize the dependent failure modeling of modules or components for interlevel failure and intra-level failure. The PoF method addresses the difficulty in obtaining measured data. This proposed approach is applied to the reliability analysis of the integrated processor system in communication equipment. The key impacted subsystems and devices are analyzed from three aspects—qualitative analysis, forward inference and backward inference—and the corresponding failure life distributions are calculated. This method can guide the improvement of system reliability and system maintenance.

复杂的电子系统的结构可能导致耦合故障机制和收集测量数据的困难。这些问题增加了可靠性分析的难度。当前的可靠性研究方法不能有效地解决上述问题。在本文中，我们提出了一种结合故障物理（PoF）方法和copula贝叶斯网络的新方法来评估复杂的电子系统。当应用于复杂电子系统的可靠性分析时，所提出的方法改善了PoF方法和传统贝叶斯网络的缺陷。copula贝叶斯网络用于为层间故障和层内故障实现模块或组件的相关故障建模。PoF方法解决了获取测量数据的困难。将该方法应用于通信设备中集成处理器系统的可靠性分析。从定性分析，正向推理和反向推理三个方面分析了受影响的关键子系统和设备，并计算了相应的故障寿命分布。该方法可以指导系统可靠性和系统维护的提高。