| 基于EM-KF算法的直升机主减速器剩余寿命预测方法 |
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| 引用本文： | 孙磊,贾云献,蔡丽影,王卫国,林国语. 基于EM-KF算法的直升机主减速器剩余寿命预测方法[J]. 航空动力学报, 2015, 30(2): 431-437. DOI: 10.13224/j.cnki.jasp.2015.02.022 |
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| 作者姓名： | 孙磊 贾云献 蔡丽影 王卫国 林国语 |
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| 作者单位： | 1. 军械工程学院 装备指挥与管理系, 石家庄 050003; |
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| 基金项目： | 预研项目(51327020101) |
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| 摘 要： | 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.
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| 关 键 词： | 卡尔曼滤波 期望最大化算法 参数估计 剩余寿命预测 主减速器 |
| 收稿时间： | 2013-09-07 |
Remaining useful life estimation method of helicopter's main retarder based on EM-KF algorithm |
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| SUN Lei,JIA Yun-xian,CAI Li-ying,WANG Wei-guo and LIN Guo-yu. Remaining useful life estimation method of helicopter's main retarder based on EM-KF algorithm[J]. Journal of Aerospace Power, 2015, 30(2): 431-437. DOI: 10.13224/j.cnki.jasp.2015.02.022 |
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| Authors: | SUN Lei JIA Yun-xian CAI Li-ying WANG Wei-guo LIN Guo-yu |
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| Affiliation: | 1. Department of Equipment Command and Management, Ordnance Engineering College, Shijiazhuang 050003, China;2. 1st Laboratory, Ariation Research Institude for the Headquarters of General Staff, Beijing 100121, China;3. Ordnance Technique Research Institution, Ordnance Engineering College, Shijiazhuang 050003, China;4. Department of Research, Ordnance Engineering College, Shijiazhuang 050003, China |
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| Abstract: | 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. |
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| Keywords: | Kalman filter expectation-maximization algorithm parameter estimation remaining useful life estimation main retarder |
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