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李天伦等:基于Copula函数的热轧支持辊健康状态预测模型 .795· (杨立蜂,王亮,冯佳晨.基于PHM技术的导弹维修保障.海军航 Neurocomputing,2017,240:98 空工程学院学报,2010,25(4):447) [17]Huang H Z,Wang H K,Li Y F,et al.Support vector machine [10]Zhang D,Feng Z P.Fault diagnosis of rolling bearings based on based estimation of remaining useful life:current research status variational mode decomposition and calculus enhanced energy and future trends.J Mech Sci Technol,2015,29(1):151 operator.Chin./Eng,2016,38(9):1327 [18]Liu J,Zio E.SVM hyperparameters tuning for recursive multi- (张东,冯志鹏.基于变分模式分解和微积分增强能量算子的滚 step-ahead prediction.Neural Comput Appl,2017,38:3749 动轴承故障诊断.工程科学学报,2016,38(9):1327) [19]Gao MZ,Xu A Q,Xu Q.Online condition prediction of electronic [11]Liu Y B.Zhou Y K,Feng Z P.Application of morphological equipment based on relevance vector machine with adaptive component analysis for rolling element bearing fault diagnosis Kerel learning.JOrdnance Equip Eng.2017,38(11):108 Chin J Eng,2017,39(6):909 (高明哲,许爱强,许晴.基于aRVM的电子设备状态在线预测方 (刘永兵,周亚凯,冯志鹏.形态分量分析在滚动轴承故障诊断 法.兵器装备工程学报,2017,38(11):108) 中的应用.工程科学学报,2017,39(6):909) [20]Zermani S,Dezan C,Chenini H,et al.FPGA implementation of [12]Zhang J L,Yang J H,Tang C Q,et al.Bearing fault diagnosis by Bayesian network inference for an embedded diagnosis //2015 stochastic resonance method in periodical potential system.ChinJ IEEE Conference on Prognostics and Health Management.Austin, Eng,2018,40(8):989 2015:1 (张景玲,杨建华,唐超权,等.基于周期势系统随机共振的轴承 [21]Zermani S,Dezan C.Euler R,et al.Bayesian network-based 故障诊断.工程科学学报,2018,40(8):989) framework for the design of reconfigurable health management [13]Zhao C.Feng Z P.Localized fault identification of planetary monitors /2015 NASA/ESA Conference on Adaptive Hardware gearboxes based on multiple-domain manifold.Chin J Eng,2017, and Systems.Montreal,2015:1 39(5):769 [22]Wang T Y,Yu J B,Siegel D,et al.A similarity-based prognostics (赵川,冯志鹏.基于多域流形的行星齿轮箱局部故障识别.工 approach for remaining useful life estimation of engineered 程科学学报.2017,39(5):769) systems ll International Conference on Prognostics and Health [14]Zhang D,FengZP.Application of iterative generalized short-time Management.Denver,2008:1 Fourier transform to fault diagnosis of planetary gearboxes.Chin [23]Zhu Y P.Generalization and application of NDT technology in Emg,2017,39(4:604 roller testing.Mech Electr Eng Technol,2016,45(5):121 (张东,冯志鹏.迭代广义短时Fourier?变换在行星齿轮箱故障诊 (朱扬普.无损检测技术在轧辊检测中的推广及应用.机电工程 断中的应用.工程科学学报,2017,39(4):604) 技术2016,45(5):121) [15]Gugulothu N,Tv V,Malhotra P,et al.Predicting remaining useful [24]He A R,Shao J,Sun W Q.Theory and Practice of Shape Control. life using time series embeddings based on recurrent neural Beijing:Metallurgical Industry Press,2016 networks II ACM SIGKDD Workshop on Machine Learning for (何安瑞,邵键,孙文权.板形控制理论与实践.北京:冶金工业 Prognostics and Health Management.Halifax,2017 出版社,2016) [16]Guo L,Li N P,Jia F,et al.A recurrent neural network based health [25]Nelsen R B.An Introduction to Copulas.Technometrics,2000, indicator for remaining useful life prediction of bearings. 42(3):317(杨立峰, 王亮, 冯佳晨. 基于PHM技术的导弹维修保障. 海军航 空工程学院学报, 2010, 25(4):447) Zhang D, Feng Z P. Fault diagnosis of rolling bearings based on variational  mode  decomposition  and  calculus  enhanced  energy operator. Chin J Eng, 2016, 38(9): 1327 (张东, 冯志鹏. 基于变分模式分解和微积分增强能量算子的滚 动轴承故障诊断. 工程科学学报, 2016, 38(9):1327) [10] Liu  Y  B,  Zhou  Y  K,  Feng  Z  P.  Application  of  morphological component  analysis  for  rolling  element  bearing  fault  diagnosis. Chin J Eng, 2017, 39(6): 909 (刘永兵, 周亚凯, 冯志鹏. 形态分量分析在滚动轴承故障诊断 中的应用. 工程科学学报, 2017, 39(6):909) [11] Zhang J L, Yang J H, Tang C Q, et al. Bearing fault diagnosis by stochastic resonance method in periodical potential system. Chin J Eng, 2018, 40(8): 989 (张景玲, 杨建华, 唐超权, 等. 基于周期势系统随机共振的轴承 故障诊断. 工程科学学报, 2018, 40(8):989) [12] Zhao  C,  Feng  Z  P.  Localized  fault  identification  of  planetary gearboxes based on multiple-domain manifold. Chin J Eng, 2017, 39(5): 769 (赵川, 冯志鹏. 基于多域流形的行星齿轮箱局部故障识别. 工 程科学学报, 2017, 39(5):769) [13] Zhang D, Feng Z P. Application of iterative generalized short-time Fourier transform to fault diagnosis of planetary gearboxes. Chin J Eng, 2017, 39(4): 604 (张东, 冯志鹏. 迭代广义短时Fourier变换在行星齿轮箱故障诊 断中的应用. 工程科学学报, 2017, 39(4):604) [14] Gugulothu N, Tv V, Malhotra P, et al. Predicting remaining useful life  using  time  series  embeddings  based  on  recurrent  neural networks  // ACM SIGKDD Workshop on Machine Learning for Prognostics and Health Management. Halifax, 2017 [15] Guo L, Li N P, Jia F, et al. A recurrent neural network based health indicator  for  remaining  useful  life  prediction  of  bearings. [16] Neurocomputing, 2017, 240: 98 Huang  H  Z,  Wang  H  K,  Li  Y  F,  et  al.  Support  vector  machine based  estimation  of  remaining  useful  life:  current  research  status and future trends. J Mech Sci Technol, 2015, 29(1): 151 [17] Liu  J,  Zio  E.  SVM  hyperparameters  tuning  for  recursive  multi￾step-ahead prediction. Neural Comput Appl, 2017, 38: 3749 [18] Gao M Z, Xu A Q, Xu Q. Online condition prediction of electronic equipment  based  on  relevance  vector  machine  with  adaptive Kernel learning. J Ordnance Equip Eng, 2017, 38(11): 108 (高明哲, 许爱强, 许晴. 基于aRVM的电子设备状态在线预测方 法. 兵器装备工程学报, 2017, 38(11):108) [19] Zermani  S,  Dezan  C,  Chenini  H,  et  al.  FPGA  implementation  of Bayesian  network  inference  for  an  embedded  diagnosis  //  2015 IEEE Conference on Prognostics and Health Management. Austin, 2015: 1 [20] Zermani  S,  Dezan  C,  Euler  R,  et  al.  Bayesian  network-based framework  for  the  design  of  reconfigurable  health  management monitors  //  2015 NASA/ESA Conference on Adaptive Hardware and Systems. Montreal, 2015: 1 [21] Wang T Y, Yu J B, Siegel D, et al. A similarity-based prognostics approach  for  remaining  useful  life  estimation  of  engineered systems  // International Conference on Prognostics and Health Management. Denver, 2008: 1 [22] Zhu  Y  P.  Generalization  and  application  of  NDT  technology  in roller testing. Mech Electr Eng Technol, 2016, 45(5): 121 (朱扬普. 无损检测技术在轧辊检测中的推广及应用. 机电工程 技术, 2016, 45(5):121) [23] He A R, Shao J, Sun W Q. Theory and Practice of Shape Control. Beijing: Metallurgical Industry Press, 2016 (何安瑞, 邵键, 孙文权. 板形控制理论与实践. 北京: 冶金工业 出版社, 2016) [24] Nelsen  R  B.  An  Introduction  to  Copulas. Technometrics,  2000, 42(3): 317 [25] 李天伦等: 基于 Copula 函数的热轧支持辊健康状态预测模型 · 795 ·
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