[1]
|
张龙,张磊,熊国良,等.基于多尺度熵的滚动轴承Elman神经网络故障诊断方法[J].机械科学与技术,2014,33(12):1854-1858 Zhang L, Zhang L, Xiong G L, et al. Rolling bearing fault diagnosis based on multiscale entropy and Elman neural network[J]. Mechanical Science and Technology for Aerospace Engineering, 2014,33(12):1854-1858 (in Chinese)
|
[2]
|
Dong S J, Luo T H. Bearing degradation process prediction based on the PCA and optimized LS-SVM model[J]. Measurement, 2013,46(9):3143-3152
|
[3]
|
肖文斌,陈进,周宇,等.小波包变换和隐马尔可夫模型在轴承性能退化评估中的应用[J].振动与冲击,2011,30(8):32-35 Xiao W B, Chen J, Zhou Y, et al. Wavelet packet transform and hidden Markov model based bearing performance degradation assessment[J]. Journal of Vibration and Shock, 2011,30(8):32-35 (in Chinese)
|
[4]
|
张龙,黄文艺,熊国良,等.基于TESPAR与GMM的滚动轴承性能退化评估[J].仪器仪表学报,2014,35(8):1772-1779 Zhang L, Huang W Y, Xiong G L, et al. Bearing performance degradation assessment based on TESPAR and GMM[J]. Chinese Journal of Scientific Instrument, 2014,35(8):1772-1779 (in Chinese)
|
[5]
|
Tax D M J, Duin R P W. Support vector data description[J]. Machine Learning, 2004,54(1):45-66.
|
[6]
|
李凌均,韩捷,郝伟,等.支持向量数据描述用于机械设备状态评估研究[J].机械科学与技术,2005,24(12):1426-1429 Li L J, Han J, Hao W, et al. Condition evaluation for mechanical equipment by means of support vector data description[J]. Mechanical Science and Technology, 2005,24(12):1426-1429 (in Chinese)
|
[7]
|
潘玉娜,陈进.结合循环平稳和支持向量数据描述的轴承性能退化评估研究[J].机械科学与技术,2009,28(4):442-445 Pan Y N, Chen J. Assessment of bearing performance degradation by cyclostationarity analysis and support vector data description[J]. Mechanical Science and Technology for Aerospace Engineering, 2009,28(4):442-445 (in Chinese)
|
[8]
|
Zhu X R, Zhang Y Y, Zhu Y S. Bearing performance degradation assessment based on the rough support vector data description[J]. Mechanical Systems and Signal Processing, 2013,34(1-2):203-217
|
[9]
|
潘玉娜,陈进,李兴林.奇异谱熵在滚动轴承性能退化评估中的应用研究[J].振动与冲击,2012,31(S):107-109 Pan Y N, Chen J, Li X L. Singular spectral entropy applied to rolling bearing performance degradation assessment[J]. Journal of Vibration and Shock, 2012,31(S):107-109 (in Chinese)
|
[10]
|
王玉梅,董洋洋,刘兴艳.高阶小波包奇异谱熵在故障选线中的应用研究[J].电力系统保护与控制,2011,39(8):23-27 Wang Y M, Dong Y Y, Liu X Y. Study on higher order wavelet packet singular entropy and its application to faulty line selection[J]. Power System Protection and Control, 2011,39(8):23-27 (in Chinese)
|
[11]
|
Nikolaou N G, Antoniadis I A. Rolling element bearing fault diagnosis using wavelet packets[J]. NDT & E International, 2002,35(3):197-205
|
[12]
|
谭善文,秦树人,汤宝平.小波基时频特性及其在分析突变信号中的应用[J].重庆大学学报(自然科学版),2001,24(2):12-17 Tan S W, Qin S R, Tang B P. Time-frequency characteristic of wavelet base and its application transient signal detection[J]. Journal of Chongqing University (Natural Science Edition), 2001,24(2):12-17 (in Chinese)
|
[13]
|
张遂强,郝伟,李志农.基于全信息技术的自适应报警方法研究[J].机械科学与技术,2006,25(12):1499-1502 Zhang S Q, Hao W, Li Z N. Study of an adaptive alarm method based on full information technique[J]. Mechanical Science and Technology, 2006,25(12):1499-1502 (in Chinese)
|
[14]
|
Qiu H, Lee J, Lin J, et al. Wavelet filter-based weak signature detection method and its application on rolling element bearing prognostics[J]. Journal of Sound and Vibration, 2006,289(4-5):1066-1090
|
[15]
|
陈斌,阎兆立,程晓斌.基于SVDD和相对距离的设备故障程度预测[J].仪器仪表学报,2011,32(7):1558-1563 Chen B, Yan Z L, Cheng X B. Machinery fault trend prediction based on SVDD and relative distance[J]. Chinese Journal of Scientific Instrument, 2011,32(7):1558-1563 (in Chinese)
|