Electro-magnetic-acoustic transducers for automatic monitoring and health assessment of transmission lines. Shoureshi, R. A., Lim, S. W., Dolev, E., & Sarusi, B. Journal of Dynamic Systems Measurement and Control-Transactions of the Asme, 126(2):303–308, June, 2004. WOS:000223382800008
doi  abstract   bibtex   
This paper presents analysis, design, development, and experimental verification of a non-destructive monitoring system for diagnosis of mechanical integrity of electric conductors based on the concept of Electro-Magnetic-Acoustic Transducers (EMAT). Electric conductors, in general, are exposed to harsh environments. Such conductors include electric transmission lines, anchor rods, and ground mat risers. For automatic failure detection and assessment of mechanical integrity of these conductors, in addition to an effective transducer feature extraction and pattern recognition techniques have to be employed. Details of the sensor design, neural-based signature analysis, feature extraction, and experimental results of fault detection techniques are presented.
@article{shoureshi_electro-magnetic-acoustic_2004,
	title = {Electro-magnetic-acoustic transducers for automatic monitoring and health assessment of transmission lines},
	volume = {126},
	issn = {0022-0434},
	doi = {10.1115/1.1767849},
	abstract = {This paper presents analysis, design, development, and experimental verification of a non-destructive monitoring system for diagnosis of mechanical integrity of electric conductors based on the concept of Electro-Magnetic-Acoustic Transducers (EMAT). Electric conductors, in general, are exposed to harsh environments. Such conductors include electric transmission lines, anchor rods, and ground mat risers. For automatic failure detection and assessment of mechanical integrity of these conductors, in addition to an effective transducer feature extraction and pattern recognition techniques have to be employed. Details of the sensor design, neural-based signature analysis, feature extraction, and experimental results of fault detection techniques are presented.},
	language = {English},
	number = {2},
	journal = {Journal of Dynamic Systems Measurement and Control-Transactions of the Asme},
	author = {Shoureshi, R. A. and Lim, S. W. and Dolev, E. and Sarusi, B.},
	month = jun,
	year = {2004},
	note = {WOS:000223382800008},
	keywords = {neural networks, torsional modes},
	pages = {303--308}
}

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