Research on Gearbox Wearing Prognosis Based on Gamma-State Space Model
Gear wearing is the result of physical and chemical effect and it is also the ultimate reason of indirect condition information changes. On one hand, the condition information which can directly reflect the gear wear degree is difficult to measure. On the other hand, there is a mount of indirect condition information that is obtained by condition monitoring system which can reflect the health of gears. On the basis of the full lifetime experiment of gearbox and a few direct condition information and plentiful indirect condition information, established the Gamma-State Space Model (SSM) which was used in analyzing the gear wear states and predicting the development of wearing out processes. Furthermore it brings forward a parameter estimation method which combines Experience Maximization (EM) algorithm and Particle Filter (PF) together. Finally, by comparing the prognostic results with the experiment, the efficiency of the model is validated.
Particle Filtering Experience Maximization Gamma process State Space Model
Yingbo Zhang Xinhui Zhao Wei Liu Jianrong Zhang Yunxian Jia Tianle Feng
Equipment Command and Management Department Ordnance Engineering College Shijiazhuang, China.050003 Teaching and Research Section of Combat Simulation Artillery Command Academy Xuanhua, China.075100 Military Representative Office in NO.617 Factory Baotou, China.014032
国际会议
贵阳
英文
279-283
2011-06-12（万方平台首次上网日期，不代表论文的发表时间）