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• W2023272615 abstract "Online induction machine faults diagnosis is a concern to guarantee the overall production process efficiency. Nowadays, the industry demands the integration of smart wireless sensors networks (WSN) to improve the fault detection in order to reduce cost, maintenance and power consumption. Induction motors can develop one or more faults at the same time that can produce sever damages. The origin of most recurrent faults in rotary machines is in the components: stator, rotor, bearing and others. This work presents a novel methodology for the online faults diagnosis in induction motors. This technique uses the smart WSN to obtain the machine condition based on the motor stator current analysis. The implementation of the proposed smart sensor methodology allows the system to perform online fault detection in a fully automated way. Simulation results presented show the efficiency of the proposed method to detect simple and multiple faults in induction machine. It provides detailed analysis to address challenges in designing and deploying WSNs in industrial environments, and its reliability." @default.
• W2023272615 created "2016-06-24" @default.
• W2023272615 creator A5038257296 @default.
• W2023272615 creator A5047539770 @default.
• W2023272615 creator A5082208649 @default.
• W2023272615 date "2015-01-01" @default.
• W2023272615 modified "2023-09-23" @default.
• W2023272615 title "Smart wireless sensor networks for online faults diagnosis in induction machine" @default.
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• W2023272615 doi "https://doi.org/10.1016/j.compeleceng.2014.10.015" @default.
• W2023272615 hasPublicationYear "2015" @default.
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