An integrated monitoring and diagnostic system for roller earings

International Journal of Advanced Manufacturing Technology

Volumn 12, Issue 1, 1996, Pages 37-46

  Huang, Hsin Hao ^a   Wang, H P Ben ^b  

^a NATIONAL FORMOSA UNIVERSITY   (Taiwan)

^b FLORIDA STATE UNIVERSITY   (United States)

Author keywords

Artificial neural Network; Autoregressive lic; Machine monitoring

Indexed keywords

ARTIFICIAL NEURAL NETWORK; AUTO REGRESSIVE MODELS; AUTOREGRESSIVE LIC; EXPERIMENTAL APPARATUS; FAULT CLASSIFICATION; INTEGRATED MACHINES; INTEGRATED MONITORING; LABORATORY EXPERIMENTS; MACHINE FAULTS; MACHINE MONITORING; ORIGINAL SIGNAL; PERFORMANCE STATISTICS; PHYSICAL MODEL; PRODUCTION MACHINES; VIBRATION SIGNAL;

ACCELEROMETERS; CURRICULA; FUZZY LOGIC; MODELS; PERSONAL COMPUTERS; TEACHING;

NEURAL NETWORKS;

EID: 79953732109     PISSN: 02683768     EISSN: 14333015     Source Type: Journal    
DOI: 10.1007/BF01178960     Document Type: Article

References (16)

1. National Research Council (U.S.), The Competitive Edge: Research Priorities for US Manufacturing, National Academy Press, Washington, DC, 1991.
2. S. M. Wu, T. H. Tobin, Jr and M. C. Chow, "Signature analysis for mechanical systems via dynamic data system (DDS) monitoring technique", Journal of Mechanical Design, 102, pp. 217-221, April 1980. (Pubitemid 11456031)
3. S. Y. Liang and D. A. Dornfeld, "Tool wear detection using time series analysis of acoustic emission", Journal of Engineering for Industry, 111, pp. 199-205, August 1989.
4. J. Tranter, "The fundamentals of, and the application of computer to, condition monitoring and predictive maintenance", Proceedings of the 1st International Machinery Monitoring and Diagnostics Conference and Exhibit, Las Vegas, NV, pp. 394-401, September 1989.
5. P. Duggan and G. K. Ashley, II, "Artificial intelligence (expert systems): a tool for predictive maintenance", Proceedings of the American Power Conference, Chicago, IL, pp. 562-566, 14-16 April, 1986.
6. C. J. Li and S. M. Wu, "On-line detection of localized defects in bearings by pattern recognition analysis", Journal of Engineering for Industry, 111, pp. 331-336, November 1989. (Pubitemid 20696373)
7. J. Marks and M. A. Elbestawi, "Development of machine tool condition monitoring system using pattern recognition", Sensors and Controls for Manufacturing: presented at the Winter Annual Meeting of the ASME, Chicago, IL, pp. 89-98, 27 November-2 December, 1988.
8. G. M. Knapp and H.-P. Wang, "Machine fault classification: aneural network approach", International Journal of Production Research, 30(4), pp. 811-823, 1992.
9. M. Caudill, "Using neural nets: hybrid expert networks", AI Expert, pp. 49-54, November 1990.
10. H. H. Huang and H.-P. Wang, "Machine fault classification using an ART 2 neural network", International Journal of Advanced Manufacturing Technology, May 1992.
11. C. C. Lin and H. P. Wang, "Classification of autoregressive spectral estimated signal patterns using an adaptive resonance theory (ART)", Computers in Industry, 22(2), pp. 143-158, 1993.
12. H. H. Huang, H.-P. Wang and G. W. Fischer, "ARTMAP neural networks for closed-loop welding process control", in Z. Dong (ed.) Artificial Intelligence in Optimal Design and Manufacturing, Prentice Hall, Englewood Cliffs, New Jersey, pp. 259-284, 1994.
13. G. A. Carpenter, S. Grossberg and J. Reynolds, "ARTMAP: supervised reaj-time learning and classification of nonstationary data by a self-organizing neural network", Neural Networks, 4, pp. 569-588, 1991.
14. S. Braun, Mechanical Signature Analysis: Theory and Applications, Academic Press, London, 1986.
15. J. Sandy, "Monitoring and diagnostics for rolling element bearings", Sound and Vibration, pp. 16-20, June 1988. (Pubitemid 18648241)
16. J. Li, Z. Ding and W. Lei, "Fuzzy cluster analysis and fuzzy pattern recognition methods for formation of part families (NAMRC)", Society of Manufacturing Engineers, pp. 558-563, 1988.