A reconfigurable watchdog Agent® for machine health prognostics

International Journal of COMADEM

Volumn 11, Issue 3, 2008, Pages 2-15

  Liao, Linxia ^a,b   Wang, Haixia ^a,c   Lee, Jay ^{a,b,c,d,e,f,g,h,i,j}  

^a 705 Engineering Research Center   (United States)

^b NATIONAL SCIENCE FOUNDATION   (United States)

^c UNIVERSITY OF CINCINNATI   (United States)

^d CRANFIELD UNIVERSITY   (United Kingdom)

^e LULEÅ UNIVERSITY OF TECHNOLOGY   (Sweden)

^f SHANGHAI JIAO TONG UNIVERSITY   (China)

^g UNIVERSITY OF MANCHESTER   (United Kingdom)

^h CITY UNIVERSITY OF HONG KONG   (Hong Kong)

ⁱ City Univ of Hong Kong ^*

^j HARBIN INSTITUTE OF TECHNOLOGY   (China)

more..

Author keywords

Machine health assessment; Prognostics; Watchdog Agent toolbox

Indexed keywords

AUTOMATIC SELECTIONS; AUTOMATIC TOOLS; DIAGNOSTICS AND PROGNOSTICS; EMBEDDED ALGORITHMS; MACHINE HEALTH ASSESSMENT; MACHINE HEALTHS; MULTIPLE CRITERIONS; PERFORMANCE ASSESSMENTS; PREDICTIVE MAINTENANCE TECHNOLOGIES; PROGNOSTICS; PROGNOSTICS AND HEALTH MANAGEMENTS; RECONFIGURABLE; TOOL SELECTIONS;

CONDITION MONITORING; CUSTOMER SATISFACTION; FEATURE EXTRACTION; MACHINERY; QUALITY FUNCTION DEPLOYMENT; SIGNAL PROCESSING;

HEALTH;

EID: 57349197195     PISSN: 13637681     EISSN: None     Source Type: Journal    
DOI: None     Document Type: Article

References (30)

1. Jardine, A.K.S., Lin, D., and Banjevic, D. (2006). A review on machinery diagnostics and prognostics implementing condition-based maintenance, Mechanical Systems and Signal Processing. Vol. 20. No. 7. 1483-1510.
2. Reichard, K.M., Van Dyke, M., and Maynard, K. (2000). Application of sensor fusion and signal classification techniques in a distributed machinery condition monitoring system, in Proceedings of SPIE The International Society for Optical Engineering.
3. Roemer, M.J., Kacprzynski, G.J., and Orsagh, R.F. (2001). Assessment of data and knowledge fusion strategies for prognostics and health management, in IEEE Aerospace Conference Proceedings.
4. Yen, G.G. and Lin, K.-C (2000). Wavelet packet feature extraction for vibration monitoring. Industrial Electronics, IEEE Transactions. Vol. 47. No. 3.650-667.
5. Kemerait, R.C. (1987). New Cepstral Approach for Prognostic Maintenance of Cyclic Machinery, in Proceedings of IEEE SOUTHEASTCON.
6. Goodenow, T., Hardman, W., and Karchnak, M. (2000). Acoustic emissions in broadband vibration as an indicator of bearing stress, in IEEE Aerospace Conference Proceedings.
7. Mcintosh, I.B.D., Mitchell, J.W., and Beckman, W.A. (2000). Fault detection and diagnosis in chillers - Part I: model development and application, ASHRAE Transactions. Vol. 106.
8. Yan, J. and Lee, J. (2005). Degradation assessment and fault modes classification using logistic regression, Journal of Manufacturing Science and Engineering, Transactions of the ASME. Vol. 127. No. 4. 912-914.
9. Poyhonen, S., Jover, P., and Hyotyniemi, H. (2004). Signal processing of vibrations for condition monitoring of an induction motor, in International Symposium on Control, Communications and Signal Processing, ISCCSP.
10. Gu, B., Wang, Z.Y., and Jing, B.Y. (2003). Fault detection and diagnosis in chillers using artificial neural networks, in Proceedings of the 2003 International Conference on Cryogenics and Refrigeration.
11. Atlas, L., Ostendorf, M., and Bernard, G.D. (2000). Hidden Markov models for monitoring machining toolwear, in ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing Proceedings.
12. Hansen, R.J., Hall, D.L., and Kurtz, S.K. (1994). New approach to the challenge of machinery prognostics, in American Society of Mechanical Engineers.
13. http://www.imscenter.net.
14. Lebold, M., et al. (2002). A Framework for Next Generation Machinery Monitoring and Diagnostics, in Failure Prevention Technology to Improve the Bottom Line, 56th Meeting of the Society for Machinery Failure Prevention Technology.
15. Casoetto, N., et al. (2003). Multisensor process performance assessment through use of autoregressive modeling and feature maps, Journal of Manufacturing Systems. Vol. 22. No. 1. 64-72.
16. Djurdjanovic, D., et al. (2000). Computerized classification of temporomandibular joint sounds, IEEE Transactions on Biomedical Engineering. Vol. 47. No. 8.977-984.
17. Lee, J. (1996). Measurement of machine performance degradation using a neural network model, Computers in Industry. Vol. 30. No. 3. 193-209.
18. Govers, C.P.M. (1996). What and how about quality function deployment (QFD), International Journal of Production Economics. Vol. 46-47. 575-585.
19. Armacost, R.L., et al. (1994). AHP framework for prioritizing customer requirements in QFD: An industrialized housing application, HE Transactions (Institute of Industrial Engineers). Vol. 26. No. 4. 7279.
20. Paul, D. (1995). Condition monitoring and defect diagnosis in manufacturing process using DDA and wavelets, in Dep. Elect. Eng., Michigan Technol. Univ.: Houghton, MI.
21. Goumas, S.K., Zervakis, M.E., and Stavrakakis, G.S. (2002). Classification of washing machines vibration signals using discrete wavelet analysis for feature extraction, IEEE Transactions on Instrumentation and Measurement. Vol. 51. No. 3. 497-508.
22. Theodoridis, S. and Koutroumbas (2003). Pattern Recognition. 2 ed., Academic Press.
23. Duda, R.O., Hart, P.E., and Stork, D.G. (2001). Pattern Classification, 2nd ed, John Wiley & Sons, New York.
24. Sun, Z. and Chang, C.C. (2002). Structural damage assessment based on wavelet packet transform, Journal of Structural Engineering. Vol. 128.No. 10. 1354-1361.
25. Yan, J., Lee, J., and Koç, M. (2002). Predictive algorithm for machine degradation detection using logistic regression, in 5th International Conference on Managing innovations in Manufacturing, Proceedings of MIM.
26. Fei, Z., Ticlin, S., and Tao, H. (2005). Fault diagnosis of motor bearing using self-organizing maps, in Proceedings of the Eighth International Conference on Electrical Machines and Systems.
27. Kohonen, T. (1990). The self-organizing map, Proceedings of the IEEE. Vol. 78. No. 9. 1464-1480.
28. Tabbane, N., Tabbane, S., and Mehaoua, A. (2004). Autoregressive, Moving Average and Mixed Autoregressive-Moving Average Process for Forecasting Qos in Ad Hoc Networks for Real-time Service Support, in Vehicular Technology Conference.
29. Pandit, S.M. and Wu, S.M. (1993). Time Series and System Analysis with Applications, John Wiley, New York.
30. Leonard, J.A. and Kramer, M.A. (1993). Diagnosing dynamic faults using modular neural nets, IEEE Expert. Vol. 8. No. 2. 44-53.