The Design of Remote Fault Diagnosis System in Engine Room

Article Preview

Abstract:

To improve the level of ship monitoring, ease the engineer’s working strength, improve the level of the shipping company regulation, the remote fault diagnosis system of ship’s engine room is designed with the comprehensive utilization of neural network, SQL Sever database, C/S structure, the Inmarsat system, etc. The system includes the data acquisition, data storage, the display and control of interface and wireless transmission. The results of application show that the system is stable, easy to use, modification with low cost, helpful for engineer knowing the state of engine room, convenient for unified supervision of the shipping company, improves the work efficiency, safeguards the shipping service.

You might also be interested in these eBooks

Info:

Periodical:

Pages:

1971-1976

Citation:

Online since:

May 2012

Export:

Price:

Permissions CCC:

Permissions PLS:

Сopyright:

© 2012 Trans Tech Publications Ltd. All Rights Reserved

Share:

Citation:

[1] Cacoullos, T. (1966). Estimation of a multivariate density. Annalsof the Institute of Statistical Mathematics (Tokyo), 18(2), 179-189.

Google Scholar

[2] Cover, T. M., & Hart, P. E. (1967). Nearest neighbor pattern classification. IEEE Transactions on bifurcation Theory, IT-13, 21-27.

DOI: 10.1109/tit.1967.1053964

Google Scholar

[3] Maloney, P. S. (1988, October). An application ofprobabilistic neural networks to a hull-to-emitter correlation problem, Paperpresented at the 6th Annual Intelligence Community AI Symposium,Washington, DC.Marchctte, D., & Priebe, C. (1987). An application of neural networks to a data fusion problem. Proceedings, 1987 Tri-Service Data Fusion Symposium 1, 23l-235.

Google Scholar

[4] Meisel, W. S. (1972). Computer-oriented approaches to pattern recognition. New York: Academic Press.Mood, A. M., & Graybill, F. A. (1962). Introduction to the theory ofstatis'tics. New York: Macmillan.Murthy, V. K. (1965). Estimation of probability density. Annals of Mathematical Statistics, 36. 1027-1031.

DOI: 10.1214/aoms/1177700074

Google Scholar

[5] Murthy, V. K. (1966). Nonparametric estimation of multivariate densities with applications. In P. R. Krishnaiah (Ed.), Multivariate anah,sis (pp.43-58). New York: Academic Press.

Google Scholar

[6] Parzen, E. (1962). On estimation of a probability density function and mode. Annals of Mathematical Statistics, 33, 1065-1076.

DOI: 10.1214/aoms/1177704472

Google Scholar

[7] Rumelhart, D. E., McClelland, J. L., & the PDP Research Group(1986). Parallel distributed processing, Volume 1 : Foundations.Cambridge, MA: The MIT Press.

Google Scholar

[8] Specht, D. E (1967a). Generation of polynomial discriminant functions for pattern recognition. IEEE Transactions" on Electronic Computers, EC-16, 3tl8-319.

DOI: 10.1109/pgec.1967.264667

Google Scholar

[9] Specht. D. E (1967b). Vectorcardiographic diagnosis using the polynomial discriminant method of pattern recognition. IEEETransactions on Bio-Medical Engineering, BME-14, 90-95. Specht, D. E

DOI: 10.1109/tbme.1967.4502476

Google Scholar