Smart wireless sensor networks for online faults diagnosis in induction machine. (January 2015)

• Record Type: Journal Article
• Authors: Guesmi, Hattab; Ben Salem, Samira; Bacha, Khmais
• Is Part Of: Computers & electrical engineering. Volume 41(2015)
• Page Start: 226
• Page End: 239
• Publication Date: 2015-01
• Subjects: WSN -- Induction motor -- Fault diagnosis -- Smart sensor -- MCSA -- Online monitoring; Computer engineering -- Periodicals; Electrical engineering -- Periodicals; Electrical engineering -- Data processing -- Periodicals; Ordinateurs -- Conception et construction -- Périodiques; Électrotechnique -- Périodiques; Électrotechnique -- Informatique -- Périodiques; Computer engineering; Electrical engineering; Electrical engineering -- Data processing; Periodicals; Electronic journals; 621.302854
• DOI: 10.1016/j.compeleceng.2014.10.015
• Languages: English
• ISSNs: 0045-7906

Abstract

Graphical abstract: Highlights: A new method has been proposed for online faults diagnosis in induction motors based on smart WSN combined with motor current signature analysis using FFT. The proposed method is novel as it is important to install low cost sensors and detection mechanisms along with induction machines to achieve short detection time and an automated way of reporting the fault. The system can distinguish a faulty motor from a healthy motor with a probability of 99% with less than 5% of false alarm. Simulation results presented show the efficiency of the proposed method to detect faults in induction machine. 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.