为解决高可靠性设备的剩余寿命预测问题,针对寿命数据缺少、物理模型难以建立的情况,结合单调退化数据,采用逆高斯退化模型,对设备的退化过程进行建模;通过参数估计的方法得到退化模型,进而预测设备的剩余寿命。在有同批次多组数据都能对逆高斯模型进行参数估计的情况下,将会面临数据融合问题。采用基于证据推理(ER)的方法对多源数据进行融合处理,引入属性权重的概念,以此更加准确地估计逆高斯模型的参数。最后,通过实验仿真,证明了所提方法能够得到较为可信的参数估计结果。

The problem of residual life prediction of high-reliability device was studied.Considering the lack of life data and the difficulty in establishing a physical model,we used the inverse Gaussian regression model to build up the degradation process model of device in combination with the monotonous degradation data.Then we obtained the degradation model by using the method of parameter estimation for forecasting the residual life of device.The problem of data fusion may emerge when there are data of multiple sets in the same batch to estimate inverse Gaussian model parameter.We used the method based on Evidential Reasoning (ER) to fuse the multi-source data,and put forward the concept of attribute weights,in order to estimate the inverse Gaussian model parameters more accurately.Finally,simulation experiment,proved that the presented method can obtain more reliable parameter estimation results.