NDLI: AC motor fault diagnosis at incipient stage using programmable logic controller

Content Provider	IEEE Xplore Digital Library
Author	Irfan, M. Saad, N. Ibrahim, R. Asirvadam, V.S.
Copyright Year	2013
Description	Author affiliation: Dept. of Electr. & Electron. Eng., Univ. Teknol. PETRONAS, Bander Sei Iskander, Malaysia (Irfan, M.; Saad, N.; Ibrahim, R.; Asirvadam, V.S.)
Abstract	Alternating Current (AC) motors are extensively used in the industries and it is essential to protect these motors from unexpected breakdowns and to detect faults at incipient level so that planned maintenance could be done. In the last three decades various fault diagnosis techniques has been developed but these techniques are considered intrusive as they require accessability to motor for data collection. Also the electromechanical devices, sensors and integrated circuits used in these techniques are expensive and not reliable. Special attention is given to non-invasive techniques to detect defects without accessing the machines. This paper attempts to provide a new approach for condition monitoring of AC motors through intelligent fault diagnosis based on programmable logic controllers. The stator current analysis which is non-invasive and economical method is used with LabVIEW signal processing tools. The current signal is analyzed via Fast Fourier Power spectrum to detect the bearing faults at incipient stages. The technique is validated at three different speeds.
Starting Page	25
Ending Page	30
File Size	681855
Page Count	6
File Format	PDF
e-ISBN	9781467346917
DOI	10.1109/CSUDET.2013.6670979
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2013-05-30
Publisher Place	Malaysia
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Fault diagnosis Induction motor Condition monitoring Induction motors Data acquisition Rotors Programmable logic controller Maintenance engineering Stators Signal processing Intelligent diagnostic
Content Type	Text
Resource Type	Article

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