[1]LOUTAS T H, ROULIAS D, GEORGOULAS G.Remaining Useful Life Estimation in Rolling Bearings Utilizing Data-driven Probabilistic E-support Vectors Regression[J]. IEEE Transactions on Reliability,2013,62(4):821-832.
[2]PAN Y, CHEN J, GUO L. Robust Bearing Performance Degradation Assessment Method Based on Improved Wavelet Packet-support Vector Data Description[J]. Mechanical Systems & Signal Processing,2009,23(3):669-681.
[3]ZHOU J, GUO H, ZHANG L, et al. Bearing Performance Degradation Assessment Using Lifting Wavelet Packet Symbolic Entropy and SVDD[J]. Shock and Vibration,2016(6):1-10.
[4]WANG H, CHEN J. Performance Degradation Assessment of Rolling Bearing Based on Bispectrum and Support Vector Data Description[J]. Journal of Vibration & Control,2013,20(13):2032-2041.
[5]YU J. A Hybrid Feature Selection Scheme and Self-organizing Map Model for Machine Health Assessment[J]. Applied Soft Computing,2011,11(5):4041-4054.
[6]CHEN F, TANG B, CHEN R. A Novel Fault Diagnosis Model for Gearbox Based on Wavelet Support Vector Machine with Immune Genetic Algorithm[J]. Measurement,2013,46(1):220-232.
[7]ZHANG L, ZHOU W, JIAO L. Wavelet Support Vector Machine.[J]. IEEE Transactions on Systems Man & Cybernetics Part B Cybernetics A Publication of the IEEE Systems Man & Cybernetics Society,2004,34(1):34-39.
[8]DONG S, TANG B, CHEN R. Bearing Running State Recognition Based on Non-extensive Wavelet Feature Scale Entropy and Support Vector Machine[J]. Measurement,2013,46(10):4189-4199.
[9]LIN W Y. A Novel 3D Fruit Fly Optimization Algorithm and Its Applications in Economics[J]. Neural Computing and Applications,2016,27(5):1391-1413.
[10]SI L, WANG Z, LIU X, et al. Identification of Shearer Cutting Patterns Using Vibration Signals Based on a Least Squares Support Vector Machine with an Improved Fruit Fly Optimization Algorithm[J]. Sensors,2016,16(1):1-21.
[11]SHEN Z, HE Z, CHEN X, et al. A Monotonic Degradation Assessment Index of Rolling Bearings Using Fuzzy Support Vector Data Description and Running Time[J]. Sensors,2012,12(8):10109-10135.
[12]ZHANG B, ZHANG L, XU J. Degradation Feature Selection for Remaining Useful Life Prediction of Rolling Element Bearings[J]. Quality & Reliability Engineering International,2016,32(2):547-554.
[13]YE S, CHEN D, YU J. A Targeted Change-detection Procedure by Combining Change Vector Analysis and Post-classification Approach[J]. Isprs Journal of Photogrammetry & Remote Sensing,2016,114:115-124.
[14]潘登. 面向轨道车辆传动系统的异常检测方法及其在滚动轴承中的应用[D]. 成都:电子科技大学,2015.
PAN Deng. Anamoly Detection for Railway Vehicle Transmission Systems and Its Application to Rolling Bearings[D]. Chengdu: University of El-ectronic Science and Technology of China,2015.
[15]张龙, 黄文艺, 熊国良,等. 基于多域特征与高斯混合模型的滚动轴承性能退化评估[J]. 中国机械工程,2014,25(22):3066-3072.
ZHANG Long, HUANG Wenyi, XIONG Guoliang. Assessment of Rolling Bearing Performance Degradation Using Gauss Mixture Model and Multi-domain Features[J]. China Mechanical Engineering,2014,25(22):3066-3072.
[16]AMES Research Center. IMS, University of Cincinnati “Bearing Data Set”, NASA Ames Prognostics Data Repository[OB/EL].[2017-03-20]. https://ti.arc.nasa.gov/tech/dash/pcoe/prognostic-data-rep ository/.
[17]田福庆,罗荣,贾兰俊,等.机械故障非平稳特征提取方法及其应用[M].北京:国防工业出版社,2014.
TIAN Fuqing, LUO Rong, JIA Lanjun, et al. Method and Application of Non-stationary Feature Extraction for Mechanical Faults[M]. Beijing: National Defense Industry Press,2014.
[18]徐清瑶. 基于支持向量数据描述的滚动轴承性能退化评估[D]. 南昌:华东交通大学,2015.
XU Qingyao. Rolling Bearing Performance Degradation Assessment Based on Support Vector Data Description[D]. Nanchang: East China Jiaotong University,2015. |