基于劣化数据的综合传动装置剩余寿命预测

研究不确定测量多维劣化监测数据下的综合传动装置剩余寿命预测.采用主元分析与状态空间模型融合得到装置劣化程度指标；根据随机过程首中时间的概念定义了装置的剩余寿命，利用Wiener过程建立了装置劣化过程模型，模型中考虑了装置劣化随机性与监测数据测量不确定性；采用极大似然估计方法估计了模型参数，并利用Kalman滤波技术实现了劣化模型的实时估计与更新，得到了装置的剩余寿命分布.研究结果表明，文中的方法能够客观描述装置性能劣化规律，优于不考虑测量不确定性的方法，能够提高剩余寿命预测的准确性，为装置的视情维护提供指导. 

Remaining Useful Life Prediction of Power-Shift Steering Transmission Based on Degradation Data

Remaining useful life (RUL) prediction of power-shift steering transmission(PSST) was presented based on multidimensional degradation monitoring data under uncertain measurement. The state space model and principle component analysis (PCA) was used to establish the degradation degree index. The RUL of PSST was defined based on the concept of first hit time (FHT) of stochastic process, and a PSST''s degradation model was established based on Wiener process, considering the stochastic degradation and uncertain measurement. And then the maximum likelihood method was utilized to estimate the model parameter. The Kalman filtering technique was used to estimate and update the degradation state, and the RUL distribution was derived. Test results show that the proposed method can objectively describe the degradation law of the PSST, which is superior to the method without considering uncertain measurement, and can improve the accuracy of RUL prediction, which is helpful to the condition based maintenance.