【摘要】 信号处理技术和信息融合技术是实时可靠度预测的关键。针对传统方法通常适用于特定的随机过程和数据分布的缺陷,提出一种新的实时可靠度预测方法。该方法采用一种基于卡尔曼滤波的噪声辅助技术来计算表征系统性能退化趋势的故障指示器,利用粒子滤波技术外推电路系统的伪失效性能,采用基于贝叶斯估计方法的信息融合技术更新性能分布的时变参数,从而预测电路的实时可靠度。引入基于真实的嵌入式平面电容器来加速退化实验所统计产生的电容器失效物理模型,并以真实数据代替理想仿真假设数据的例子,验证了结合噪声辅助技术和现场数据的实时可靠度预测方法的有效性。结果表明,现场数据信息越多,电路的实时可靠度预测准确性越高。更多还原

【Abstract】 Signal processing and information fusion technologies are the key to real-time reliability prediction. The traditional real-time reliability analysis methods are based on specific random process and probability distribution. A new real-time reliability prediction method is proposed. The proposed method uses a Kalman filter-based noise-assisted technique to calculate the fault indicators that characterize system performance degradation trend. On this basis,the particle filter technology is used to extrapolate the pseudo-failure performance of circuit system,and then the Bayesian inference as an information fusion method is introduced to update the time-varying parameters of performance distribution,thus predicting the real-time reliability of the circuit. The failure physical model of an embedded planar capacitor based on the real acceleration degradation experiment is introduced,and the effectiveness of this real-time prediction method combined with noise-assisted technology and on-site data is verified by using real data instead of ideal simulation hypothetical data. The result shows that the more the on-site data information is,the higher the prediction accuracy of circuit reliability is.更多还原