基于状态的装备故障预测技术综述
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摘要
综述了基于状态的装备故障预测与维修决策的发展现状,分别将近年来基于可靠性的故障预测、基于数据驱动的故障预测、基于统计的故障预测、基于失效物理的故障预测、综合集成故障预测5类故障预测的文献进行了总结,并分析了故障预测中的不确定性。对装备故障预测研究现状进行了分析与展望。
A review was summarized of the development situation of the equipment fault prediction and maintenance decision based on condition as well as the literatures about fault prediction based on reliability, fault prediction based on data driven, fault prediction based on statistics, fault prediction based on failure physics and integrated fault prediction respectively in the recent years, with the uncertainty in the failure prediction analyzed at the same time.An analysis and An outlook were conducted of the research status of the equipment fault prediction and maintenance decision.
A review was summarized of the development situation of the equipment fault prediction and maintenance decision based on condition as well as the literatures about fault prediction based on reliability, fault prediction based on data driven, fault prediction based on statistics, fault prediction based on failure physics and integrated fault prediction respectively in the recent years, with the uncertainty in the failure prediction analyzed at the same time.An analysis and An outlook were conducted of the research status of the equipment fault prediction and maintenance decision.
引文
[1] 陈涛,孙伟,张旭.基于灰色关联度的风电齿轮箱传动系统故障树分析[J].太阳能学报,2012,33(10):1655-1660.
[2] 党香俊,姜同敏.基于相关分析和组合神经网络的退化预测[J].北京航空航天大学学报,2013,39(1):42-51.
[3] 杜党波,张伟,胡昌华,等.含缺失数据的小波-卡尔曼滤波故障预测方法[J].自动化学报,2014,40(10):2115-2125.
[4] 蔡志强,孙树栋,司书宾,等.基于FMECA的复杂装备故障预测贝叶斯网络建模[J].系统工程理论与实践,2012,33(1):187-193.
[5] 冯辅周,司爱威,江鹏程.小波相关排列熵和HMM在故障预测中的应用[J].振动工程学报,2013,26(2):269-276.
[6] 徐宇亮,孙际哲,陈西宏,等.电子设备健康状态评估与故障预测方法[J].系统工程与电子技术,2012,34(5):1068-1072.
[7] 邓力,范庚,刘治学.基于回归分析方法的网络故障预测[J].计算机工程,2012,38(20):251-255.
[8] 李向前.复杂装备故障预测与健康管理关键技术研究[D].北京:北京理工大学,2014.
[9] 杜占龙,李小民,郑宗贵,等.强跟踪平方根容积卡尔曼滤波和自回归模型融合的故障预测[J].控制理论与应用,2014,31(5):1047-1052.
[10] 郑战光,蔡敢为,李兆军.一种新的疲劳损伤演化模型[J].工程力学,2010,27(2):37-40.
[11] 刘文光,陈国平.考虑裂纹表面摩擦阻尼的振动疲劳裂纹扩展分析[J].振动与冲击,2012,31(5):42-45.
[12] 薛飞,余伟炜,蒙新明,等.国产压水堆核电站机组主管道疲劳裂纹扩展特性实验研究[J].中国电机工程学报,2014,34(8):1310-1317.
[13] 王爱红,徐格宁,高有山.桥式起重机随机应力谱获取及疲劳剩余寿命估算[J].机械工程学报,2012,48(18):192-198.
[14] 王艳.Ti6A14V基体及其改性试样的冲击磨损行为分析[J].功能材料,2012,43(17):2393-2396.
[15] 赵新泽,高伟,赵美云,等.干燥和酸性条件下钢芯铝绞线磨损特性研究[J].摩擦学学报,2011,31(6):616-621.
[16] 冯培锋,王殿富,杜善义,等.层板复合材料疲劳性能的唯象学研究模型及其应用[J].材料科学与工程,2002,20(2):192-195.
[17] 顾怡.复合材料拉伸剩余强度及其分布[J].南京航空航天大学学报,1999,31(2):164-171.
[18] 苏小萍.CFRP层板的疲劳损伤研究方法[J].纤维复合材料,2004,20(3):60-63.
[19] 赵建忠,叶文,张磊.基于数据融合和改进新陈代谢不等间距GM(1,1)模型的导弹装备故障预测[J].兵工学报,2014,35(10):1689-1695.
[20] 赵建忠,李海军,叶文,等.基于信息融合和改进UCG(1,1)模型的故障预测[J].系统工程与电子技术,2013,35(10):2135-2140.
[21] 高甜容,于东,岳东峰.基于故障先兆判定模型和动态置信度匹配的主轴润滑故障预测方法[J].机械工程学报,2012,48(17):75-82.
[22] 曾庆虎,邱静,刘冠军,等.基于KPCA-HSMM设备状态退化识别与故障预测方法研究[J].仪器仪表学报,2009,30(7):1341-1346.
[23] 费胜巍,孙宇.融合粗糙集与灰色理论的电力变压器故障预测[J].中国电机工程学报,2008,28(16):154-460.
[24] JAOUHER B A,BRIGITTE Chebel-Morello,LOTFI S,et al.Accurate bearing remaining useful life prediction based on Weibull distribution and artificial neural network[J].Mechanical Systems and Signal Processing,2015,56:150-172.
[25] CUI Yiqian,SHI Junyou,WANG Zili.Discrete Event Logistics Systems(DELS) simulation modeling incorporating two-step Remaining Useful Life (RUL) estimation[J].Computers in Industry,2015,72:68-81.
[26] GARCIA Nieto PJ,GARCIA-Gonzalo E,SANCHEZ Lasheras F,et al.Hybrid PSO-SVM-based method for forecasting of the remaining useful life for aircraft engines and evaluation of its reliability[J].Reliability Engineering and System Safety,2015,138(6):219-231.
[27] LIU Datong,ZHOU Jianbao,PAN Dawei,et al.Lithiu-mion battery remaining useful life estimation with an optimized relevance vector machine algorithm with incremental learning[J].Measurement,2015,63:143-151.
[28] 孙强,岳继光.基于不确定性的故障预测方法综述[J].控制与决策,2014,29(5):769-778.
[29] 许喆平,郎荣玲,邓小乐.飞机性能参数预测的不确定性处理[J].航空学报,2012,33(6):1100-1107.
[30] 邓宏琛,张勤,赵越.DUCG在核电站故障诊断中的推理机设计与实现[J].原子能科学技术,2014,48(增刊1):491-495.
[31] 张磊,李行善,于劲松,等.基于关联向量机回归的故障预测算法[J].系统工程与电子技术,2010,32(7):1540-1543.
[32] 蔡志强,司书宾,孙树栋,等.基于贝叶斯网络的不确定环境装备故障推理模型[J].西北工业大学学报,2011,29(4):509-514.
[33] 徐宇亮,孙际哲,陈西宏,等.基于加速退化试验和粒子滤波的电子设备故障预测方法[J].航空学报,2012,33(8):1483-1490.
[34] ZHU Shunpeng,HUANG Hongzhong,REUEL Smith,et al.Bayesian framework for probabilistic low cycle fatigue life prediction and uncertainty modeling of aircraft turbine disk alloys[J].Probabilistic Engineering Mechanics,2013,34:114-122.
[35] WU Xuedong,ZHU Zhiyu,FAN Shaosheng,et al.Fai-lure and reliability prediction of engine systems using ite-rated nonlinear filters based state-space least square support vector machine method[J].Optik,2016,127(3):1491-1496.
[36] ZHU Shunpeng,HUANG Hongzhong,PENG Weiwen,et al.Probabilistic physics of failure-based framework for fatigue life prediction of aircraft gas turbine discs under uncertainty[J].Reliability Engineering and System Safety,2016,146:1-12.
[2] 党香俊,姜同敏.基于相关分析和组合神经网络的退化预测[J].北京航空航天大学学报,2013,39(1):42-51.
[3] 杜党波,张伟,胡昌华,等.含缺失数据的小波-卡尔曼滤波故障预测方法[J].自动化学报,2014,40(10):2115-2125.
[4] 蔡志强,孙树栋,司书宾,等.基于FMECA的复杂装备故障预测贝叶斯网络建模[J].系统工程理论与实践,2012,33(1):187-193.
[5] 冯辅周,司爱威,江鹏程.小波相关排列熵和HMM在故障预测中的应用[J].振动工程学报,2013,26(2):269-276.
[6] 徐宇亮,孙际哲,陈西宏,等.电子设备健康状态评估与故障预测方法[J].系统工程与电子技术,2012,34(5):1068-1072.
[7] 邓力,范庚,刘治学.基于回归分析方法的网络故障预测[J].计算机工程,2012,38(20):251-255.
[8] 李向前.复杂装备故障预测与健康管理关键技术研究[D].北京:北京理工大学,2014.
[9] 杜占龙,李小民,郑宗贵,等.强跟踪平方根容积卡尔曼滤波和自回归模型融合的故障预测[J].控制理论与应用,2014,31(5):1047-1052.
[10] 郑战光,蔡敢为,李兆军.一种新的疲劳损伤演化模型[J].工程力学,2010,27(2):37-40.
[11] 刘文光,陈国平.考虑裂纹表面摩擦阻尼的振动疲劳裂纹扩展分析[J].振动与冲击,2012,31(5):42-45.
[12] 薛飞,余伟炜,蒙新明,等.国产压水堆核电站机组主管道疲劳裂纹扩展特性实验研究[J].中国电机工程学报,2014,34(8):1310-1317.
[13] 王爱红,徐格宁,高有山.桥式起重机随机应力谱获取及疲劳剩余寿命估算[J].机械工程学报,2012,48(18):192-198.
[14] 王艳.Ti6A14V基体及其改性试样的冲击磨损行为分析[J].功能材料,2012,43(17):2393-2396.
[15] 赵新泽,高伟,赵美云,等.干燥和酸性条件下钢芯铝绞线磨损特性研究[J].摩擦学学报,2011,31(6):616-621.
[16] 冯培锋,王殿富,杜善义,等.层板复合材料疲劳性能的唯象学研究模型及其应用[J].材料科学与工程,2002,20(2):192-195.
[17] 顾怡.复合材料拉伸剩余强度及其分布[J].南京航空航天大学学报,1999,31(2):164-171.
[18] 苏小萍.CFRP层板的疲劳损伤研究方法[J].纤维复合材料,2004,20(3):60-63.
[19] 赵建忠,叶文,张磊.基于数据融合和改进新陈代谢不等间距GM(1,1)模型的导弹装备故障预测[J].兵工学报,2014,35(10):1689-1695.
[20] 赵建忠,李海军,叶文,等.基于信息融合和改进UCG(1,1)模型的故障预测[J].系统工程与电子技术,2013,35(10):2135-2140.
[21] 高甜容,于东,岳东峰.基于故障先兆判定模型和动态置信度匹配的主轴润滑故障预测方法[J].机械工程学报,2012,48(17):75-82.
[22] 曾庆虎,邱静,刘冠军,等.基于KPCA-HSMM设备状态退化识别与故障预测方法研究[J].仪器仪表学报,2009,30(7):1341-1346.
[23] 费胜巍,孙宇.融合粗糙集与灰色理论的电力变压器故障预测[J].中国电机工程学报,2008,28(16):154-460.
[24] JAOUHER B A,BRIGITTE Chebel-Morello,LOTFI S,et al.Accurate bearing remaining useful life prediction based on Weibull distribution and artificial neural network[J].Mechanical Systems and Signal Processing,2015,56:150-172.
[25] CUI Yiqian,SHI Junyou,WANG Zili.Discrete Event Logistics Systems(DELS) simulation modeling incorporating two-step Remaining Useful Life (RUL) estimation[J].Computers in Industry,2015,72:68-81.
[26] GARCIA Nieto PJ,GARCIA-Gonzalo E,SANCHEZ Lasheras F,et al.Hybrid PSO-SVM-based method for forecasting of the remaining useful life for aircraft engines and evaluation of its reliability[J].Reliability Engineering and System Safety,2015,138(6):219-231.
[27] LIU Datong,ZHOU Jianbao,PAN Dawei,et al.Lithiu-mion battery remaining useful life estimation with an optimized relevance vector machine algorithm with incremental learning[J].Measurement,2015,63:143-151.
[28] 孙强,岳继光.基于不确定性的故障预测方法综述[J].控制与决策,2014,29(5):769-778.
[29] 许喆平,郎荣玲,邓小乐.飞机性能参数预测的不确定性处理[J].航空学报,2012,33(6):1100-1107.
[30] 邓宏琛,张勤,赵越.DUCG在核电站故障诊断中的推理机设计与实现[J].原子能科学技术,2014,48(增刊1):491-495.
[31] 张磊,李行善,于劲松,等.基于关联向量机回归的故障预测算法[J].系统工程与电子技术,2010,32(7):1540-1543.
[32] 蔡志强,司书宾,孙树栋,等.基于贝叶斯网络的不确定环境装备故障推理模型[J].西北工业大学学报,2011,29(4):509-514.
[33] 徐宇亮,孙际哲,陈西宏,等.基于加速退化试验和粒子滤波的电子设备故障预测方法[J].航空学报,2012,33(8):1483-1490.
[34] ZHU Shunpeng,HUANG Hongzhong,REUEL Smith,et al.Bayesian framework for probabilistic low cycle fatigue life prediction and uncertainty modeling of aircraft turbine disk alloys[J].Probabilistic Engineering Mechanics,2013,34:114-122.
[35] WU Xuedong,ZHU Zhiyu,FAN Shaosheng,et al.Fai-lure and reliability prediction of engine systems using ite-rated nonlinear filters based state-space least square support vector machine method[J].Optik,2016,127(3):1491-1496.
[36] ZHU Shunpeng,HUANG Hongzhong,PENG Weiwen,et al.Probabilistic physics of failure-based framework for fatigue life prediction of aircraft gas turbine discs under uncertainty[J].Reliability Engineering and System Safety,2016,146:1-12.