Prognosis of the probability of failure in tool condition monitoring application-a time series based approach

International Journal of Advanced Manufacturing Technology

Volumn 76, Issue 1-4, 2015, Pages 513-521

  Wu, Yue ^a   Hong, G S ^a   Wong, W S ^a  

^a NATIONAL UNIVERSITY OF SINGAPORE   (Singapore)

Author keywords

Bayesian multilayer perceptron; Delay coordinate embedding; Prognostics; Tool condition monitoring

Indexed keywords

APPROXIMATION THEORY; CONDITION MONITORING; CUTTING; FORECASTING; MULTILAYER NEURAL NETWORKS; MULTILAYERS; WEAR OF MATERIALS;

BAYESIAN; DELAY COORDINATE EMBEDDING; FUNCTION APPROXIMATION; LOCAL LINEAR MODELING; MAINTENANCE INTERVALS; PROBABILITY OF FAILURE; PROGNOSTICS; TOOL CONDITION MONITORING;

CUTTING TOOLS;

EID: 84921264618     PISSN: 02683768     EISSN: 14333015     Source Type: Journal    
DOI: 10.1007/s00170-014-6299-0     Document Type: Article

References (16)

1. Qu J, Zuo MJ (2012) An LSSVR-based algorithm for online system condition prognostics. Expert Syst Appl 39(5):6089–6102. doi:10.1016/j.eswa.2011.12.002
2. Caesarendra W, Widodo A, Yang B-S (2011) Combination of probability approach and support vector machine towards machine health prognostics. Probabilistic Eng Mech 26(2):165–173. doi:10.1016/j.probengmech.2010.09.008
3. Liao W, Wang Y, Pan E (2012) Single-machine-based predictive maintenance model considering intelligent machinery prognostics. Int J Adv Manuf Technol 63(1–4):51–63. doi:10.1007/s00170-011-3884-3
4. Lin D, Makis V (2003) Recursive filters for a partially observable system subject to random failure. Adv Appl Probab 35:207–227, Copyright 2003, IEE
5. Wang WQ, Golnaraghi MF, Ismail F (2004) Prognosis of machine health condition using neuro-fuzzy systems. Mech Syst Signal Process 18:813–831, Compendex
6. Yam RCM, Tse PW, Li L, Tu P (2001) Intelligent predictive decision support system for condition-based maintenance. Int J Adv Manuf Technol 17:383–391, Copyright 2001, IEE
7. Baruah P, Chinnam RB (2005) HMMs for diagnostics and prognostics in machining processes. Int J Prod Res 43(6):1275–1293
8. Kunpeng Z (2007) Multi-categories tool wear classification in micro-milling. NUS, Singapore
9. Packard NH, Crutchfield JP, Farmer JD, Shaw RS (1980) Geometry from a time series. Phys Rev Lett 45:712–716, Copyright 1980, IEE
10. Zhang J, Shu-Hung Chung H, Wai-Lun L (2008) Chaotic time series prediction using a neuro-fuzzy system with time-delay coordinates. IEEE Trans Knowl Data Eng 20:956–964, Copyright 2008, The Institution of Engineering and Technology
11. Sauer T (1994) Time series prediction by using delay coordinate embedding (data set A). (Compendex):175-175
12. Kennel MB, Brown R, Abarbanel HDI (1992) Determining embedding dimension for phase-space reconstruction using a geometrical construction. Phys Rev A Stat Phys Plasmas Fluids Relat Interdiscip Top 45:3403–3411, Copyright 1992, IEE
13. Casdagli M, Weigend AS (1994) Exploring the continuum between deterministic and stochastic modeling (data sets A,B,D). (Compendex):347-347
14. Shao Y, Nezu K (2000) Prognosis of remaining bearing life using neural networks. Proc Inst Mech Eng I-J Syst 214(13):217–230
15. Wang P, Vachtsevanos G (2001) Fault prognostics using dynamic wavelet neural networks. AI EDAM Artif Intell Eng Des Anal Manuf 15:383–391
16. Chinnam RB, Baruah P (2004) A neuro-fuzzy approach for estimating mean residual life in condition-based maintenance systems. Int J Mater Prod Technol 20:166–179