NDLI: Motor fault detection using vibration patterns

Content Provider	IEEE Xplore Digital Library
Author	Rahman, M.K.M. Azam, T. Saha, S.K.
Copyright Year	2010
Description	Author affiliation: Department of Electrical and Electronic Engineering, United International University, Dhanmondi, Dhaka, Bangladesh (Rahman, M.K.M.; Azam, T.; Saha, S.K.)
Abstract	This work presents a novel method for fault detection of electrical motors using vibration signal. Most of the motor faults generate specific patterns in the motor vibration that can be captured and analyzed for diagnosis. Early detection of motor faults can save the motor from subsequent deteriorations into more severe conditions, and thus can save lot of maintenance costs. In our work, an optical mouse was used to capture decently accurate information of the motor-vibration. Features are extracted in time and frequency domain using which an Artificial Neural Network (ANN) called Multi-Layer Perceptron (MLP) was trained to learn different motor conditions such as healthy and faulty. A MATLAB-based user interface was developed to record, monitor, analyze and classify the motor vibration data. This study shows that using simple features and ANN structure can effectively and efficiently classify different types of motor faults. The use of low-cost mouse sensor has made this method very attractive to wide range of applications where a cost-effective solution is desired.
Starting Page	486
Ending Page	489
File Size	835707
Page Count	4
File Format	PDF
ISBN	9781424462773
e-ISBN	9781424462803
DOI	10.1109/ICELCE.2010.5700735
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2010-12-18
Publisher Place	Bangladesh
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Feature extraction Induction motors Vibrations Artificial neural networks Mice Fault detection Frequency domain analysis Multi-Layer Perceptron Motor-Fault Detection Optical Mouse Vibration Analysis Neural Network
Content Type	Text
Resource Type	Article

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1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
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