This research presents an intelligent and automated diagnostic framework for early detection of stator and bearing faults in three-phase induction motors (IMs), which are vital components in industrial, commercial, and residential systems. Combining experimental data with advanced AI techniques like fuzzy logic, neural networks, and support vector machines, the study develops high-accuracy models for stator fault classification. For bearing fault diagnosis, a multi-stage methodology is introduced using statistical time-domain features, KPCA-SVM classifiers, and a novel Adaptive Modified Morlet Wavelet (AMMW) transform. The proposed techniques demonstrate excellent performance even in noisy conditions, offering a highly reliable solution for real-time IM fault monitoring and predictive maintenance.
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Taschenbuch. Etat : Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - This research presents an intelligent and automated diagnostic framework for early detection of stator and bearing faults in three-phase induction motors (IMs), which are vital components in industrial, commercial, and residential systems. Combining experimental data with advanced AI techniques like fuzzy logic, neural networks, and support vector machines, the study develops high-accuracy models for stator fault classification. For bearing fault diagnosis, a multi-stage methodology is introduced using statistical time-domain features, KPCA-SVM classifiers, and a novel Adaptive Modified Morlet Wavelet (AMMW) transform. The proposed techniques demonstrate excellent performance even in noisy conditions, offering a highly reliable solution for real-time IM fault monitoring and predictive maintenance. N° de réf. du vendeur 9789999329750
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Paperback. Etat : new. Paperback. This research presents an intelligent and automated diagnostic framework for early detection of stator and bearing faults in three-phase induction motors (IMs), which are vital components in industrial, commercial, and residential systems. Combining experimental data with advanced AI techniques like fuzzy logic, neural networks, and support vector machines, the study develops high-accuracy models for stator fault classification. For bearing fault diagnosis, a multi-stage methodology is introduced using statistical time-domain features, KPCA-SVM classifiers, and a novel Adaptive Modified Morlet Wavelet (AMMW) transform. The proposed techniques demonstrate excellent performance even in noisy conditions, offering a highly reliable solution for real-time IM fault monitoring and predictive maintenance. This item is printed on demand. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability. N° de réf. du vendeur 9789999329750
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Taschenbuch. Etat : Neu. STATOR AND BEARING FAULTS DIAGNOSIS OF INDUCTION MOTOR | Om Prakash Yadav | Taschenbuch | Englisch | 2025 | Eliva Press | EAN 9789999329750 | Verantwortliche Person für die EU: Libri GmbH, Europaallee 1, 36244 Bad Hersfeld, gpsr[at]libri[dot]de | Anbieter: preigu Print on Demand. N° de réf. du vendeur 134358585
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