基于退化数据与失效数据的导弹雷达导引头竞争失效状态预测
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摘要
针对导弹雷达导引头测试数据获取困难、失效机理多样的问题,在分析导弹雷达导引头退化数据和失效数据特点的基础上,提出了一种退化失效与突发失效相关的竞争失效状态预测模型。针对退化数据的小样本、非线性和不确定性的特点,采用相关向量机(RVM)对其分布参数序列进行回归预测,并基于量子粒子化算法(QPSO)和Hannan-Quinn(H-Q)准则分别对核参数和嵌入维数进行寻优;考虑到突发失效与退化失效之间的相关性,引入位置-尺度模型描述突发失效分布参数与退化数据间的相关关系,进而对下一阶段导弹雷达导引头的失效状态进行预测。实例预测结果验证了模型的可行性及合理性。
Considering the problems of "difficult acquisition of test data and diverse failure mechanism" of the radar seeker of a missile?we proposed a competition failure state prediction model related to degraded failure and sudden failure based on the analysis to the characteristics of the degraded data and failure data.Aiming at the characteristics of small sample?nonlinearity?and uncertainty of the degraded data?we used Relevance Vector Machine(RVM) to regress and predict the distributed parameter sequence.Then the kernel parameter and embedded dimensions were both optimized via Quantum-Behaved Particle Swarm Optimization(QPSO) and Hannan-Quinn(H-Q) criterion.Taking into account the correlation between the sudden failure and the degraded failure?a position-scale model was introduced to describe the correlation between the sudden failure distribution parameters and the degraded data?thus to predict the failure state of the next stage.Prediction result of an example verified the feasibility and rationality of the model.
Considering the problems of "difficult acquisition of test data and diverse failure mechanism" of the radar seeker of a missile?we proposed a competition failure state prediction model related to degraded failure and sudden failure based on the analysis to the characteristics of the degraded data and failure data.Aiming at the characteristics of small sample?nonlinearity?and uncertainty of the degraded data?we used Relevance Vector Machine(RVM) to regress and predict the distributed parameter sequence.Then the kernel parameter and embedded dimensions were both optimized via Quantum-Behaved Particle Swarm Optimization(QPSO) and Hannan-Quinn(H-Q) criterion.Taking into account the correlation between the sudden failure and the degraded failure?a position-scale model was introduced to describe the correlation between the sudden failure distribution parameters and the degraded data?thus to predict the failure state of the next stage.Prediction result of an example verified the feasibility and rationality of the model.
引文
[1] 丛林虎,徐廷学,王骞,等.基于退化数据与故障数据的导弹竞争故障预测[J].北京航空航天大学学报,2016,42(3):522-530.
[2] 冯鹏飞,朱永生,王培功,等.基于相关向量机模型的设备运行可靠性预测[J].振动与冲击,2017,36(12):146-149,180.
[3] 周建宝,王少军,马丽萍,等.可重构卫星锂离子电池剩余寿命预测系统研究[J].仪器仪表学报,2013,34(9):2034- 2044.
[4] 徐公国,段修生,徐宏浩,等.基于Rényi信息增量和改进QPSO算法的多传感器协同分配[J].电光与控制,2017,24(5):15-19.
[5] 范庚,马登武,吴明辉,等.电子系统状态时间序列预测的优化相关向量机方法[J].系统工程与电子技术,2013,35(9):2011-2015.
[6] 邴其春,龚勃文,杨兆升,等.一种组合核相关向量机的短时交通流局域预测方法[J].哈尔滨工业大学学报,2017,49(3):144-149.
[7] 逯程,徐廷学,张海军,等.改进RVM的装备退化状态预测方法[J].现代防御技术,2018,46(2):153-158.
[8] 温艳清,刘宝亮.完全数据下Weibull分布参数的极大似然估计[J].应用数学,2008,21(s1):67-70.
[2] 冯鹏飞,朱永生,王培功,等.基于相关向量机模型的设备运行可靠性预测[J].振动与冲击,2017,36(12):146-149,180.
[3] 周建宝,王少军,马丽萍,等.可重构卫星锂离子电池剩余寿命预测系统研究[J].仪器仪表学报,2013,34(9):2034- 2044.
[4] 徐公国,段修生,徐宏浩,等.基于Rényi信息增量和改进QPSO算法的多传感器协同分配[J].电光与控制,2017,24(5):15-19.
[5] 范庚,马登武,吴明辉,等.电子系统状态时间序列预测的优化相关向量机方法[J].系统工程与电子技术,2013,35(9):2011-2015.
[6] 邴其春,龚勃文,杨兆升,等.一种组合核相关向量机的短时交通流局域预测方法[J].哈尔滨工业大学学报,2017,49(3):144-149.
[7] 逯程,徐廷学,张海军,等.改进RVM的装备退化状态预测方法[J].现代防御技术,2018,46(2):153-158.
[8] 温艳清,刘宝亮.完全数据下Weibull分布参数的极大似然估计[J].应用数学,2008,21(s1):67-70.
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