基于EM-KF算法的直升机主减速器剩余寿命预测方法

为解决间接状态监测数据下直升机主减速器剩余寿命预测难以估算的难题,提出了一种卡尔曼滤波和期望最大化算法相结合的剩余寿命预测方法.该方法可以根据不断更新振动信号特征值迅速且有效地估计出模型参数,进而预测不同运行时间主减速器的剩余寿命分布,最后对主减速器试验数据进行了案例分析.结果表明:该方法能够有效估计主减速器的剩余寿命分布,通过与主减速器剩余寿命准确值对比发现,剩余寿命准确值绝大多数落于剩余寿命预测值的95%置信区间内,表明该方法具有好的准确性,进而避免故障的发生.

Remaining useful life estimation method of helicopter's main retarder based on EM-KF algorithm

A remaining useful life prediction method was proposed based on Kalman filter and expectation-maximization algorithm to solve the estimation problem of remaining useful life of helicopter's main retarder with introduction of indirect condition information. The proposed method can estimate the model parameters rapidly and effectively by updating characteristic value of vibration signal, and then remaining useful life distribution of main retarder was estimated at different working times. Finally, the case analysis was conducted through the test data of main retarder. The results demonstrate that the proposed method can effectively estimate the remaining useful life distribution of main retarder. Through comparison of real remaining useful life and estimated remaining useful life of main retarder, it is found that the estimated remaining useful life is mainly contained within 95% confidence interval, showing the proposed method has well accuracy, which can avoid the occurrence of the fault.