针对部分可观测信息条件下量测噪声未知时粒子滤波剩余寿命预测的问题, 提出了一种基于集成神经网络和粒子滤波的寿命预测方法。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粒子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, 选取锂离子电池寿命预测仿真验证了该方法的有效性和可行性。

Based on the method of integrated neural networks and particle filter, a new method is proposed for predicting the residual useful life based on the particle filter with unknown measurement noise under the condition of partially observable information. Firstly, a status-observation data set is generated based on the equipment performance degradation data, and multiple data sets are constructed by using bootstrap technique. The integrated neural network is used to train the status-observation data sets, and the optimal range of measurement noise standard deviation is obtained through derivation. Then, by embedding the measurement noise standard deviation into the framework of particle filter lifetime prediction as the unknown parameter, the residual life prediction and probability density distribution of the nonlinear system are realized. Finally, the validity and feasibility of the proposed method is verified by the simulation to the life of the lithium ion battery.