WU S J, TSAI T R. Estimation of time-to-failure distribution derived from a degradation model using fuzzy clustering [J]. Quality and Reliability Engineering International, 2000,16(4):261-267.

ZIO E. Reliability engineering: old problems and new challenges [J]. Reliability Engineering and System Safety, 2009,94(2):125-141.

NAGI Z G, MARK A L. A neural network degradation model for computing and updating residual life distributions [J]. IEEE Transactions on Automation Science and Engineering, 2008,5(1):154-163.

CHINNAM R B,RAI B. Intelligence in reliability engineering [M]. Berlin, Germany: Springer,2007:223-260.

CAO H, CHEN X, ZI Y, et al. End milling tool breakage detection using lifting scheme and Mahalanobis distance [J]. International Journal of Machine Tools and Manufacture,2008,48(2):141-151.

HENG A, TAN A, MATHEW J, et al. Intelligent condition-based prediction of machinery reliability [J]. Mechanical Systems and Signal Processing,2009,23(5):1600-1614.

YI J, WANG Q, ZHAO D, et al. BP neural network prediction-based variable-period sampling approach for networked control systems [J]. Applied Mathematics and Computation, 2007,185(2):976-988.

LU J C, MEEKER W Q. Using degradation measures to estimation a time-to-failure distribution [J]. Technometrics, 1997,39(4):391-400.

KAPLAN E L, MEIER P. Nonparametric estimation from incomplete observations [J]. Journal of the American Statistical Association, 1958,53(2):457-481.

SICK B. On-line and indirect tool wear monitoring in turning with artificial neural networks: a review of more than a decade of research [J]. Mechanical Systems and Signal Processing, 2002,16(4):487-546.