基于 ENN 和 UKF 的电子部件剩余使用寿命预测

Residual Useful Life Prediction for Electronic Component Based on ENN and UKF

针对部分可观测信息条件下量测噪声未知时非线性滤波剩余寿命预测的问题,提出了一种基于集成神经网络(ENN)和无迹卡尔曼滤波(UKF)的寿命预测方法。首先,结合设备性能退化量测数据,生成状态-观测数据组,并利用bootstrap技术构建多个数据组,采用集成神经网络训练状态-观测数据组,根据推导公式估计量测噪声标准差的最优取值范围;其次,将量测噪声标准差作为未知参数嵌入在无迹卡尔曼滤波寿命预测框架中,实现非线性系统的剩余寿命预测及概率密度分布;最后,选取电子部件锂电池进行寿命预测仿真验证了该方法的有效性和可行性。

A new method based on the methods of ensemble neural networks and unscented Kalman filter is proposed for predicting the residual useful life based on the unscented Kalman filter with unknown measurement noise under the condi -tion of partially observable information .Firstly ,in combination with the equipment performance degradation data ,a state of the group of observed data is generated ,and the bootstrap technique is used to construct of a plurality of groups of data ,the integrated neural network training state observation data sets are adopted to estimate the measurement noise optimal range according to the formula .Secondly ,the residual life prediction and probability density distribution of the nonlinear system are realized by embedding the standard error of measurement noise as the unknown parameters into the framework of un -scented Kalman filter lifetime prediction .Finally ,the validity and feasibility of the proposed method is verified by the simula-tion of the life of the lithiumion battery .