Adaptive resampling-based particle filtering for tool life prediction

Journal of Manufacturing Systems

Volumn 37, Issue , 2015, Pages 528-534

  Wang, Peng ^a   Gao, Robert X ^a  

^a CASE WESTERN RESERVE UNIVERSITY   (United States)

Author keywords

Bayesian inference; Particle filter; Remaining useful life; Tool wear prediction

Indexed keywords

ADAPTIVE FILTERING; BAYESIAN NETWORKS; COMPUTER CONTROL SYSTEMS; FORECASTING; INFERENCE ENGINES; MONTE CARLO METHODS; PROBABILITY DISTRIBUTIONS; QUALITY CONTROL; WEAR OF MATERIALS;

BAYESIAN INFERENCE; IMPORTANCE RE SAMPLINGS; KULLBACK LEIBLER DIVERGENCE; PARTICLE FILTER; REMAINING USEFUL LIFE PREDICTIONS; REMAINING USEFUL LIVES; SAMPLE IMPOVERISHMENT; TOOL WEAR;

CUTTING TOOLS;

EID: 84928717428     PISSN: 02786125     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.jmsy.2015.04.006     Document Type: Article

References (20)

1. B. Li A review of tool wear estimation using theoretical analysis and numerical simulation technologies J Refract Metals Hard Mater 35 2012 143 151
2. P.W. Marksberry, and I.S. Jawahir A comprehensive tool-wear/tool-life performance model in the evaluation of NDM for sustainable manufacturing Int J Mach Tool Manuf 48 2008 878 886
3. P.C. Paris, M.P. Gomez, and W.E. Anderson A rational analytic theory of fatigue Trend Eng 13 1961 9 14
4. K. Aslantas, and S. Tasgetiren A study of spur gear pitting formation and life prediction Wear 257 2004 1167 1175
5. B. Mills, and A. Redford Machinability of engineering materials 1983 Applied Science London
6. E. Hoffman Fundamentals of tool design 1984 SME Dearborn
7. J. Karandikar, A. Abbas, and T. Schmitz Tool life predictions using a random walk method of Bayesian updating Mach Sci Technol 17 3 2013 410 442
8. R. Teti, K. Jemielniak, G. O'Donnell, and D. Dornfeld Advanced monitoring of machining operations CIRP Ann Manuf Technol 59 2010 717 739
9. R.E. Kalman A new approach to linear filtering and prediction problems Trans ASME J Basic Eng 82 1960 35 45
10. S. Julier, and J. Uhlmann A new extension of Kalman filter to nonlinear systems Proc. of 11th International Symposium on Aerospace/Defense sensing, Simulation and Controls, Multi Sensor Fusion, Tracking and Resource Management 1997
11. M.S. Arulampalam, S. Maskell, N. Gordon, and T. Clapp A tutorial on particle filters for online nonlinear/non-Gaussian Bayesian tracking IEEE Trans Signal Process 50 2 2002 174 188
12. A. Doucet, and A. Johansen A tutorial on particle filtering and smoothing: fifteen years later Oxford handbook of nonlinear filtering 2009 Oxford University Press Oxford 656 704
13. T. Li, S. Sun, T. Sattar, and J. Corchado Fight sample degeneracy and impoverishment in particle filters: a review of intelligent approaches Expert Syst Appl 51 2014 3944 3954
14. C. Nemeth, P. Fearnhead, and L. Mihaylova Sequential Monte-Carlo methods for state and parameter estimation in abruptly changing environments IEEE Trans Signal Process 62 5 2014 1245 1255
15. X. Li, B.S. Lim, J.H. Zhou, S. Huang, S.J. Phua, K.C. Shaw, and et al. Fuzzy neural network modelling for tool wear estimation in dry milling operation Proc. of Annual Conference of the Prognostics and Health Management Society 2009 1 11
16. J. Wang, P. Wang, and R. Gao Tool life prediction for sustainable manufacturing Proc. of 11th global conference on sustainable manufacturing 2013 230 234
17. J. Wang, P. Wang, and R. Gao Particle filter for tool wear prediction Proc. of 42nd north american manufacturing research conference 2014
18. J. Liu, and M. West Combined parameter and state estimation in simulation based filtering. Sequential Monte-Carlo methods in practice 2001 Springer New York NY 197 223
19. D.Z. Li, W. Wang, and F. Ismail A muted particle filter technique for system state estimation and battery life prediction IEEE Trans Instrum Meas 63 8 2014 2034 2043
20. S. Eguchi, and J. Copas Interpreting Kullback-Leibler divergence with the Neyman-Pearson Lemma J Multivar Anal 97 9 2006 2034 2040