Wang Jingbin, Wang Xiaohong, Wang Lizhi
School of Reliability and Systems Engineering, Beihang University, Beijing 100191, China.
Unmanned System Institute, Beihang University, Beijing 100191, China.
Sensors (Basel). 2017 Sep 15;17(9):2123. doi: 10.3390/s17092123.
Predicting system lifetime is important to ensure safe and reliable operation of products, which requires integrated modeling based on multi-level, multi-sensor information. However, lifetime characteristics of equipment in a system are different and failure mechanisms are inter-coupled, which leads to complex logical correlations and the lack of a uniform lifetime measure. Based on a Bayesian network (BN), a lifetime prediction method for systems that combine multi-level sensor information is proposed. The method considers the correlation between accidental failures and degradation failure mechanisms, and achieves system modeling and lifetime prediction under complex logic correlations. This method is applied in the lifetime prediction of a multi-level solar-powered unmanned system, and the predicted results can provide guidance for the improvement of system reliability and for the maintenance and protection of the system.
预测系统寿命对于确保产品的安全可靠运行至关重要,这需要基于多层次、多传感器信息的集成建模。然而,系统中设备的寿命特性各不相同且失效机制相互耦合,这导致了复杂的逻辑相关性以及缺乏统一的寿命度量。基于贝叶斯网络(BN),提出了一种结合多层次传感器信息的系统寿命预测方法。该方法考虑了偶然故障与退化失效机制之间的相关性,并在复杂逻辑相关性下实现系统建模和寿命预测。该方法应用于多层次太阳能无人系统的寿命预测,预测结果可为提高系统可靠性以及系统的维护和保护提供指导。