A framework of an intelligent maintenance prognosis tool

2nd International Conference on Computer Research and Development, ICCRD 2010

Volumn , Issue , 2010, Pages 241-245

  Asmai, Siti Azirah ^a   Hussin, Burairah ^a   Mohd Yusof, Mokhtar ^a  

^a UNIVERSITI TEKNIKAL MALAYSIA MELAKA   (Malaysia)

Author keywords

Component; Condition based Maintenance(CBM); Data driven; Maintenance; Prognosis; Remaining useful life (RUL)

Indexed keywords

CONDITION-BASED MAINTENANCE; CONDITION-BASED MAINTENANCE(CBM); DATA-DRIVEN; DATA-DRIVEN APPROACH; HUMAN HEALTH; INTELLIGENT MAINTENANCE; MAINTENANCE ACTION; MAINTENANCE DECISIONS; MAINTENANCE PLANS; MEDICAL AREAS; OPERATING PERFORMANCE; PRACTICAL USE; REMAINING USEFUL LIVES;

RESEARCH;

MAINTENANCE;

EID: 77955204971     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/ICCRD.2010.69     Document Type: Conference Paper

References (18)

1. A. Heng, S. Zhang, A. Tan, and J. Mathew, "Rotating machinery prognostics: State of the art, challenges and opportunities". Mechanical Systems and Signal Processing, 2009. 23(3): p. 724-739.
2. J. Luo, K. Pattipati, L. Qiao, and S. Chigusa, "Model-based prognostic techniques applied to a suspension system". IEEE Transactions on Systems, Man and Cybernetics, Part A, 2008. 38(5): p. 1156-1168.
3. W. Wang, "A two-stage prognosis model in condition based maintenance". European Journal of Operational Research, 2007. 182(3): p. 1177-1187. (Pubitemid 46729624)
4. M. Dong and D. He, "Hidden semi-Markov model-based methodology for multi-sensor equipment health diagnosis and prognosis". European Journal of Operational Research, 2007. 178(3): p. 858-878.
5. A. Jardine, D. Lin, and D. Banjevic, "A review on machinery diagnostics and prognostics implementing condition-based maintenance". Mechanical Systems and Signal Processing, 2006. 20(7): p. 1483-1510.
6. B. Al-Najjar and I. Alsyouf, "Selecting the most efficient maintenance approach using fuzzy multiple criteria decision making". International Journal of Production Economics, 2003. 84(1): p. 85-100.
7. J. Lee, H. Qiu, J. Ni, and D. Djurdjanovic. "Infotronics technologies and predictive tools for next-generation maintenance systems". 2004.
8. N. Cook, "Statistical evaluation of prognostic versus diagnostic models: beyond the ROC curve". Clinical Chemistry, 2008. 54(1): p. 17.
9. J. Lee, J. Ni, D. Djurdjanovic, H. Qiu, and H. Liao, "Intelligent prognostics tools and e-maintenance". Computers in industry, 2006. 57(6): p. 476-489.
10. V. Tran, B. Yang, M. Oh, and A. Tan, "Machine condition prognosis based on regression trees and one-step-ahead prediction". Mechanical Systems and Signal Processing, 2008. 22(5): p. 1179-1193.
11. A. Muller, M. Suhner, and B. Iung, "Formalisation of a new prognosis model for supporting proactive maintenance implementation on industrial system". Reliability Engineering and System Safety, 2008. 93(2): p. 234-253.
12. M. Schwabacher. "A survey of data-driven prognostics". 2005: Citeseer.
13. G. Vachtsevanos and P. Wang. "Fault prognosis using dynamic wavelet neural networks". 2001.
14. O. Dragomir, R. Gouriveau, and N. Zerhouni, "Adaptive Neuro-Fuzzy Inference System for mid term prognostic error stabilization". International Journal of Computers, Communications & Control, 2008. 1: p. 6.
15. J. Luo, M. Namburu, K. Pattipati, L. Qiao, M. Kawamoto, and S. Chigusa, "Model-based prognostic techniques". Update. 1: p. 2.
16. M. Abbas, A. Ferri, M. Orchard, and G. Vachtsevanos. "An intelligent diagnostic/prognostic framework for automotive electrical systems". 2007.
17. R. Kothamasu and S. Huang, "Adaptive Mamdani fuzzy model for condition-based maintenance". Fuzzy Sets and Systems, 2007. 158(24): p. 2715-2733.
18. J. Campos, "Development in the application of ICT in condition monitoring and maintenance". Computers in Industry, 2009. 60(1): p. 1-20.