Prediction of Railcar Remaining Useful Life by Multiple Data Source Fusion

IEEE Transactions on Intelligent Transportation Systems - Tập 16 Số 4 - Trang 2226-2235 - 2015
Zhiguo Li1, Qing He2
1Bus. Solutions & Math. Sci. Dept., IBM T. J. Watson Res. Center, Yorktown Heights, NY, USA
2Dept. of Civil, Struct., Environ. Eng., State Univ. of New York, Buffalo, NY, USA

Tóm tắt

Từ khóa


Tài liệu tham khảo

brence, 2006, Analysis of robust measures in random forest regression

horning, 0, Random forests: An algorithm for image classification and generation of continuous fields data sets, Proc Int Conf Geoinformat Spatial Infrastruct Develop Earth Allied Sci, 1

hastie, 1990, Generalized Additive Models

10.1093/bioinformatics/btg287

schmidt, 2005, Least Squares Optimization with L1-Norm Regularization

breiman, 2014, Package ‘randomForest’ Reference Manual

brence, 2006, Comparative Analysis of Two Forest-Based Regression Algorithms

10.1109/TITS.2010.2041057

10.1109/PHM.2008.4711437

10.1007/s00170-004-2131-6

10.1016/j.ejor.2010.11.018

10.1007/s13198-013-0195-0

benkedjouh, 2013, Health assessment and life prediction of cutting tools based on support vector regression, J Intell Manuf, 26, 1751

10.1109/TR.2013.2285318

10.1016/j.ymssp.2005.09.012

10.1016/j.wear.2006.03.025

pombo, 2010, A railway wheel wear prediction tool based on a multibody software, J Theor Appl Mech, 48, 751

meinshausen, 2006, Quantile regression forests, J Mach Learn Res, 7, 983

10.1109/RRCON.1998.668093

van buuren, 1999, Flexible Multivariate Imputation by MICE

tournay, 0, The development of algorithms to detect poorly performing vehicles at wayside detectors, Proc 8th World Congr Railway Res, 1

10.1177/1475921705049764

10.1017/CBO9780511754098

bladon, 0, Predictive condition monitoring of railway rolling stock, Proc Conf Railway Eng, 1

brickle, 2008, Identification of Existing and New Technologies for Wheelset Condition Monitoring

lagneback, 2007, Evaluation of wayside condition monitoring technologies for condition-based maintenance of railway vehicles

10.1111/j.1467-8667.2008.00584.x

robeda, 2008, Technology drives US train inspections, Int Railway J, 48, 47

schlake, 2010, Impact of Automated Condition Monitoring Technologies on Railroad Safety and Efficiency

10.1145/1081870.1081929

breiman, 1984, Classification and Regression Trees

hajibabai, 0, Wayside defect detector data mining to predict potential WILD train stops, Proc Amer Railway Eng Maintenance Way Assoc Annu Meet, 1

10.1093/bioinformatics/btr597

breiman, 0, Random Forests

schafer, 1997, Analysis of Incomplete Multivariate Data, 10.1201/9781439821862

10.1093/bioinformatics/17.6.520