The Delvotest method detection limits per manufacturer's instructions at a fixed reading time of 3 h for 24 antimicrobial agents were determined in ewe milk by photometric measurement. For each drug, eight concentrations were tested on 20 ewe milk samples from individual ewes. Detection limits, determined by means of logistic regression models, were (microg/kg): 3, amoxycillin; 2, ampicillin; 18, cloxacillin; 1, penicillin "G"; 34, cefadroxil; 430, cephalosporin "C"; 40, cephalexin; 20, cefoperazone; 33, Ceftiofur; 18, cefuroxime; 6100, streptomycin; 1200, gentamycin; 2600, neomycin; 830, erythromycin; 100, tylosin; 180, doxycycline; 320, oxytetracycline; 590, tetracycline; 88, sulfadiazine; 44, sulfamethoxazole; 140, sulfametoxypyridazine; 48, sulfaquinoxaline; 12,000, chloramphenicol; and 290, trimethoprim. Whereas the beta-lactam antibiotics, sulphonamides, and tylosin were detected by Delvotest method at levels equal to those of maximum residue limits, its sensitivity needs to be enhanced to detect aminoglycosides, tetracyclines, streptomycin, chloramphenicol, and trimethoprim residues in ewe milk or to develop an integrated residue detection system for ewe milk with different sensitive microorganisms for each group of antiinfectious agents.

The aim of this paper is to present a new approach for rotor bar failure diagnosis in induction machines. The method focuses on the study of an approximation signal resulting from the wavelet decomposition of the startup stator current. The presence of the left sideband harmonic is used as evidence of the rotor failure in most diagnosis methods based on the analysis of the stator current. Thus, a detailed description of the evolution of the left sideband harmonic during the startup transient is given in this paper; for this purpose, a method for calculating the evolution of the left sideband during the startup is developed and its results are physically explained. This paper also shows that the approximation signal of a particular level, obtained from the Discrete Wavelet Transform (DWT) of the startup stator current, practically reproduces the time evolution of the left sideband harmonic during the startup. The diagnosis method proposed here consists of checking if the selected approximation signal fits well the characteristic shape of the left sideband harmonic evolution described in the paper. The method is validated through laboratory tests. The results prove that it can constitute a useful tool for the diagnosis of rotor bar breakages. Index Terms-Broken rotor bars, fault diagnosis, startup transient, wavelet analysis. Martin Riera-Guasp received the M.Sc. degree in Industrial Engineering and the Ph.D. degree in Electrical Engineering from the Universidad Politecnica de Valencia (Spain) in 1981 and 1987, respectively. Currently he is an Associate Professor in the Department of Electrical Engineering of the Polytechnic University of Valencia. His research interests include condition monitoring of electrical machines, applications of the Wavelet Theory to electrical engineering and efficiency in electric power applications. Jose A. Antonino-Daviu received the M.Sc. degree in Electrical Engineering from the Universidad Politecnica de Valencia in 2000 and the Ph.D. degree in Electrical Engineering in 2006. He worked in the private sector, being involved in several international projects. Currently, he is Associate Professor in the School of Industrial Engineering of the mentioned University, where he develops his docent and research work. His primary research interests are condition monitoring of electric machines, wavelet theory and its application to fault diagnosis and design and optimization of electrical installations and systems.

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