[1]臧怀刚,李清志,韩艳龙,等. 基于改进的经验模式分解的滚动轴承故障诊断研究[J]. 计量学报, 2013,34(2):101-105.
[2]Huang N E, Shen Z, Long S R, et al. The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis[J]. Proceedings of the Royal Society London A, 1998, 454(1971):903-995.
[3]孟宗,顾海燕. 基于盲源分离去噪和HHT的旋转机械故障诊断研究[J]. 计量学报, 2013,34(3):242-246.
[4]谢平,王欢,杜义浩.基于EMD和Wigner-Ville分布的机械故障诊断方法研究[J]. 计量学报,2010,31(5):390-394.
[5]何旭, 经验模式分解的研究及其在故障诊断中的应用[D]. 上海:上海交通大学, 2004.
[6]Chen W T, Wang Z Z, Xie H B, et al. Characterization of surface EMG signal based on fuzzy entropy[J]. IEEE Transactions on Neural System and Rehabilitation Engineering, 2007, 15(2):266-272.
[7]Chen W T, Zhuang J, Yu W X, et al. Measuring complexity using FuzzyEn, ApEn, and SampEn[J]. Medical Engineering and Physics, 2009, 31(1):61-68.
[8]郑近德,程军圣,杨宇. 基于改进的ITD和模糊熵的滚动轴承故障诊断方法[J]. 中国机械工程, 2012,23(19):2372-2377.
[9]Yuan S F, Chu F L. Support vector machines-based fault diagnosis for turbo-pump rotor[J]. Mechanical Systems and Signal Processing, 2006, 20(4):939-952.
[10]徐玉秀,杨文平,吕轩,等. 基于支持向量机的汽车发动机故障诊断研究[J]. 计量学报, 2013,32(8): 143-146.
[11]戴桂平. 基于EMD近似熵和DAGSVM的机械故障诊断[J].计量学报, 2010,31(5): 467-471. |