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Applications Of Wavelets In Induction Machine Fault Detection

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2008 Annual Conference & Exposition


Pittsburgh, Pennsylvania

Publication Date

June 22, 2008

Start Date

June 22, 2008

End Date

June 25, 2008



Conference Session

Case Studies & Engineering Education Around the Globe

Tagged Division


Page Count


Page Numbers

13.209.1 - 13.209.10



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Paper Authors


Erick Schmitt Pennsylvania State University-Harrisburg

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Mr. Schmitt is a graduate student in the Master of Engineering, Electrical Engineering at Penn State Harrisburg. He was a teaching assistant, and Physics and Mathematics tutor. He is now a graduate assistant researching in Travel Time Prediction Models.

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Peter Idowu Pennsylvania State University-Harrisburg

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Dr. Idowu obtained his Ph.D. degree in Electrical Engineering (1989) from the University of Toledo; Toledo, Ohio; his M.S. in Electrical Engineering (1983) from the University of Bridgeport, Bridgeport, Conn and his B.S. in Electrical Engineering (1979), from the Polytechnic Ibadan, Ibadan, Nigeria. His research interests are on application of fuzzy logic, power system estimation and control using artificial neural networks, adaptive control of electric power systems, power system analysis, computer modeling of power system elements. He is an Associate Professor of Electrical Engineering at Penn State Harrisburg.

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Aldo Morales Pennsylvania State University-Harrisburg

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Dr. Morales received his Electronic Engineering degree with distinction from the University of Tarapaca, Arica, Chile, and M.S. and Ph.D. degrees in Electrical and Computer Engineering from the State University of New York at Buffalo. His research interests are digital signal and image processing, and computer vision. He is an Associate Professor of Electrical Engineering at Penn State Harrisburg.

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NOTE: The first page of text has been automatically extracted and included below in lieu of an abstract

Applications of Wavelets in Induction Machine Fault Detection Erick Schmitt, Peter Idowu, and Aldo Morales Penn State University at Harrisburg, Middletown, PA 17057


This paper presents a new wavelet-based algorithm for three-phase induction machine fault detection. This new method uses the standard deviation of wavelet coefficients, obtained from n- level decomposition of each phase, to identify single-phasing of supply and unbalanced stator resistance faults in three-phase machines. The proposed algorithm can operate independent of the operational frequency, fault type and loading conditions. Results show that this algorithm has better detection response than the Fourier Transform-based techniques. In addition, a user- friendly graphical interface was designed.

1. Introduction

Induction machines are among the most widely used devices in industrial processes today. They are generally viewed to be robust and well suited for a wide ranging applications. This increasing critical role in industrial processes underscores the level of attention given to early detection or diagnosis of potentially destructive faults, as well as the extensive research time devoted to the subject over the past decade.

Methods for prediction and detection of motor faults are extensively documented in research literatures; many of these methods use stator currents and voltage signals in some form along with signature algorithms to determine or predict fault conditions in an induction motor. A very organized summary of developments in motor signature analysis tools and techniques over the last two decades is presented by Benbouzid in1. Classical signature analysis techniques primarily use Fourier transform methods to examine current waveforms in details and then establish some criteria for classifying a range of rotor and stator faults. The trend in signature analysis is moving towards application of non-traditional computational techniques in the subject areas such as finite elements and more recently wavelet signal processing2-5. Induction motor fault diagnosis using Fourier techniques is well established6. However, the frequency resolution required dictates a need for a large amount of data.

This paper presents a novel induction motor fault detection system that does not require a large amount of data such as in the Fourier analysis techniques. The method uses wavelet analysis to classify winding related motor problems such as open winding and winding resistance. The reduction of these memory requirements allow the implementation of this system with lower cost hardware and permit the algorithm to be run in near real-time. In addition, a graphical user interface (GUI) was developed for student-friendly usage.

2. Overview of the Wavelet Transform Technique

Fourier analysis techniques provide significant information on frequency components of signals under study, but offer no information regarding where a particular frequency was located in the time axis. In contrast, wavelet transforms offers time-frequency information of signals under

Schmitt, E., & Idowu, P., & Morales, A. (2008, June), Applications Of Wavelets In Induction Machine Fault Detection Paper presented at 2008 Annual Conference & Exposition, Pittsburgh, Pennsylvania. 10.18260/1-2--3152

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