@proceedings {65967,
	title = {Intelligent Diagnostic of Induction Machine for Faults Detection and Classification Using Wavelet and Fuzzy inference},
	journal = {Applied Mechanics and MaterialsApplied Mechanics and Materials},
	volume = {799},
	year = {2015},
	month = {2015},
	pages = {964-970},
	publisher = {Trans Tech Publ},
	isbn = {3-03835-631-X},
	author = {Bourdim, Samia and Hemsas, Kamel Eddine and Harbouche, Youcef and Abdssemed, Rachid}
}
@proceedings {65968,
	title = {Multi phases stator short-circuits faults diagnosis \& classification in DFIG using wavelet \& fuzzy based technique},
	journal = {2015 3rd International Conference on Control, Engineering \& Information Technology (CEIT)},
	year = {2015},
	month = {2015},
	pages = {1-6},
	publisher = {IEEE},
	isbn = {1-4799-8212-1},
	author = {Bourdim, Samia and Hemsas, Kamel-eddine and Harbouche, Youcef and Azib, Toufik}
}
@proceedings {61515,
	title = {Hybrid technique based on Wavelet \&amp; Fuzzy inference for Induction Machine Faults diagnosis \&amp; Classification},
	journal = {IECEC{\textquoteright}15},
	year = {2015},
	address = {Setif, Algeria},
	abstract = {
	An intelligent diagnostic method based on 3-D plot continuous wavelet transform (3-D plot CWT) and fuzzy inference system is presented to investigate the detectability and classification of rotor broken bars faults in induction machine (IM) and to overcome the limitation of classical Fourier Transform (FT). This approach is successfully used with Motor Current Signature Analysis (MCSA) and suitable developed model of IM in healthy and faulty mode using Matlab environment. As first step we performed new results using 3-D plot CWT to extract the discriminating features. The features extracted from the wavelet transformed signal are the second most predominant frequency, the time range at which it occurs and the corresponding wavelet coefficients .Then as second and last step a fuzzy Inference system is designed and implemented using Matlab software with these three features extracted from the wavelet transformed signal as inputs and generates an output that classifies the fault and no

},
	url = {https://www.researchgate.net/profile/Kamel-Hemsas/publication/278016208_Hybrid_technique_based_on_Wavelet_Fuzzy_inference_for_Induction_Machine_Faults_diagnosis_Classification/links/5589b7e008ae2affe7152174/Hybrid-technique-based-on-Wavelet-Fuzzy-inferenc},
	author = {Bourdim, Samia and Hemsas, Kamel-eddine and Harbouche, Youcef}
}
