| 引用本文: | 范彬,胡雷,胡茑庆.退化速率跟踪粒子滤波在剩余使用寿命预测中的应用.[J].国防科技大学学报,2015,37(3):161-166.[点击复制] |
| FAN Bin,HU Lei,HU Niaoqing.Remaining useful life prediction based on degradation rate tracking particle filter[J].Journal of National University of Defense Technology,2015,37(3):161-166[点击复制] |
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| 退化速率跟踪粒子滤波在剩余使用寿命预测中的应用 |
| 范彬, 胡雷, 胡茑庆 |
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(国防科技大学 装备综合保障技术重点实验室，湖南 长沙 410073)
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| 摘要: |
| 毋庸置疑，剩余使用寿命预测对于设备的健康管理越来越重要。近年来粒子滤波方法被越来越多地应用到设备寿命预测技术当中，这是因为粒子滤波方法能更好地解决非线性非高斯系统滤波问题，而且能够获得不确定度信息。但该方法的预测性能却过度依赖于预测模型，并且对于模型参数的初始分布也比较敏感，这在一定程度上限制了粒子滤波预测方法的进一步发展。针对基本粒子滤波预测方法的不足，提出了一种基于退化速率跟踪粒子滤波的通用预测框架，以历史观测数据的退化速率统计规律作为指导来跟踪目标数据的退化速率，实现对粒子滤波预测方法的简化，并将该方法用于轴承和锂离子电池的剩余使用寿命预测，验证了方法的有效性。 |
| 关键词: 粒子滤波 退化速率跟踪 剩余使用寿命 预测框架 |
| DOI：10.11887/j.cn.201503026 |
| 投稿日期：2015-01-21 |
| 基金项目:国家自然科学基金资助项目（51105366，51475463）；国防科学技术大学科研计划资助项目（JC12-03-02） |
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| Remaining useful life prediction based on degradation rate tracking particle filter |
| FAN Bin, HU Lei, HU Niaoqing |
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(Laboratory of Science and Technology on Integrated Logistics Support, National University of Defense Technology, Changsha 410073, China)
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| Abstract: |
| There is no doubt that remaining useful life prediction is important to the health management of modern equipment. Particle filter method has been widely applied to the prediction of equipment remaining useful life in recent years, because it can solve the filtering problem of nonlinear and non-Gaussian systems better and it allows the uncertainty management. However, the prediction performance of a particle filter method is largely dependent on the prediction model and is very sensitive to the initial distribution of the model parameters. These flaws limit the further development of particle filter methods in the prediction to a certain extent. Aiming at the shortcomings of the basic particle filter prediction method, a kind of general prediction framework based on degradation rate tracking particle filter was presented. In the proposed method, the statistical rule of historical data was utilized to guide the degradation rate tracking of target data and simplify the prediction process. The remaining useful life prediction cases of rolling bearings and Li-ion battery verified the effectiveness of the proposed method. |
| Keywords: particle filter degradation rate tracking remaining useful life prediction framework |
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