Industrial applications require suitable monitoring systems able to identify any decrement in the production efficiency involving economical losses. The information coming from a general purpose monitoring system can be usefully exploited to implement a sensorless instrument monitoring an AC motor drive and a diagnostic tool providing useful risk coefficients. The method is based on a complex digital processing of the line signals acquired by means of a virtual instrument. In this paper a genetic algorithm, implemented in a Mathcad environment, performs the evaluation of the risk indexes from the processed line signals. The combination of genetic algorithms and neural network is also investigated as a promising possibility for the development of a reliable diagnostic tool. The risk coefficients derived from this approach are evaluated, discussed and compared to other indexes - in particular fuzzy indexes - introduced by the authors in previous papers.
A genetic algorithm for fault identification in electrical drives : a comparison with neuro-fuzzy computation / L. Cristaldi, M. Lazzaroni, A. Monti, F. Ponci, F.E. Zocchi - In: IMTC/2004 : proceedings of the 21. IEEE instrumentation and measurement technology conference : from the electrometer to the networked instruments : a giant step toward a deeper knowledge : Como, Italy, may 18-20, 2004. 2. / [a cura di] S. Demidenko ... [et al.]. - Piscataway : Institute of electrical and electronics engineers, 2004. - ISBN 078038248X. - pp. 1454-1459 (( Intervento presentato al 21. convegno Instrumentation and Measurement Technology Conference (IMTC) tenutosi a Como nel 2004 [10.1109/IMTC.2004.1351341].
A genetic algorithm for fault identification in electrical drives : a comparison with neuro-fuzzy computation
M. LazzaroniSecondo
;
2004
Abstract
Industrial applications require suitable monitoring systems able to identify any decrement in the production efficiency involving economical losses. The information coming from a general purpose monitoring system can be usefully exploited to implement a sensorless instrument monitoring an AC motor drive and a diagnostic tool providing useful risk coefficients. The method is based on a complex digital processing of the line signals acquired by means of a virtual instrument. In this paper a genetic algorithm, implemented in a Mathcad environment, performs the evaluation of the risk indexes from the processed line signals. The combination of genetic algorithms and neural network is also investigated as a promising possibility for the development of a reliable diagnostic tool. The risk coefficients derived from this approach are evaluated, discussed and compared to other indexes - in particular fuzzy indexes - introduced by the authors in previous papers.
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