A dynamic multi-scale Markov model based methodology for remaining life prediction

[1]  Shien-Ming Wu,et al.  Time series and system analysis with applications , 1983 .

[2]  Santanu Das,et al.  Force Parameters for On-line Tool Wear Estimation: A Neural Network Approach , 1996, Neural Networks.

[3]  Sigeru Omatu,et al.  Classification of bill fatigue levels by feature-selected acoustic energy pattern using competitive neural network , 2000, Proceedings of the IEEE-INNS-ENNS International Joint Conference on Neural Networks. IJCNN 2000. Neural Computing: New Challenges and Perspectives for the New Millennium.

[4]  Don-Lin Yang,et al.  An efficient Fuzzy C-Means clustering algorithm , 2001, Proceedings 2001 IEEE International Conference on Data Mining.

[5]  Giovanna Castellano,et al.  Mining categories of learners by a competitive neural network , 2001, IJCNN'01. International Joint Conference on Neural Networks. Proceedings (Cat. No.01CH37222).

[6]  J. Srinivas,et al.  Neural Networks: Algorithms and Applications , 2002 .

[7]  M. Farid Golnaraghi,et al.  Prognosis of machine health condition using neuro-fuzzy systems , 2004 .

[8]  H. Kantz,et al.  Markov chain model for turbulent wind speed data , 2004 .

[9]  Nagi Gebraeel,et al.  Residual life predictions from vibration-based degradation signals: a neural network approach , 2004, IEEE Transactions on Industrial Electronics.

[10]  Jay Lee,et al.  A prognostic algorithm for machine performance assessment and its application , 2004 .

[11]  Tzu-Li Tien.  A research on the prediction of machining accuracy by the deterministic grey dynamic model DGDM(1, 1, 1) , 2005, Appl. Math. Comput..

[12]  James M. Keller,et al.  A possibilistic fuzzy c-means clustering algorithm , 2005, IEEE Transactions on Fuzzy Systems.

[13]  Daming Lin,et al.  A review on machinery diagnostics and prognostics implementing condition-based maintenance , 2006 .

[14]  Changying Li,et al.  Neural network and Bayesian network fusion models to fuse electronic nose and surface acoustic wave sensor data for apple defect detection , 2007 .

[15]  Jay Lee,et al.  Similarity based method for manufacturing process performance prediction and diagnosis , 2007, Comput. Ind..

[16]  Wang Ya,et al.  Life prediction on protective coating of steel bridge based on gray system theory , 2007, 2007 IEEE International Conference on Grey Systems and Intelligent Services.

[17]  Lifeng Xi,et al.  Residual life predictions for ball bearings based on self-organizing map and back propagation neural network methods , 2007 .

[18]  Chen Li,et al.  Condition Residual Life Evaluation by Support Vector Machine , 2007, 2007 8th International Conference on Electronic Measurement and Instruments.

[19]  F. Ueda,et al.  A Fuzzy Model for Estimating the Remaining Lifetime a Diesel Engine , 2007, NAFIPS 2007 - 2007 Annual Meeting of the North American Fuzzy Information Processing Society.

[20]  Yin Zongrun,et al.  Life Prediction of Electronic Equipment Based on Optimum Model of GM(1,1) , 2008, CSSE 2008.

[21]  Ronny Mai,et al.  Thermal–mechanical fatigue behaviour and life prediction of oxide dispersion strengthened nickel-based superalloy PM1000 , 2008 .

[22]  Nagi Gebraeel,et al.  A Neural Network Degradation Model for Computing and Updating Residual Life Distributions , 2008, IEEE Transactions on Automation Science and Engineering.

[23]  C. Dalle Donne,et al.  Fatigue life predictions using fracture mechanics methods , 2009 .

[24]  Joseph Mathew,et al.  Rotating machinery prognostics. State of the art, challenges and opportunities , 2009 .

[25]  Jihong Yan,et al.  A FCM-weighted markov model for remaining life prediction , 2009, 2009 IEEE International Conference on Automation and Logistics.