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Publication Metadata only Adjustable-Speed Drive Bearing-Fault Detection Via Wavelet Packet Decomposition(IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC, 2009) Teotrakool, Kaptan; Devaney, Michael J.; Eren, Levent; University of Missouri System; University of Missouri Columbia; Bahcesehir UniversityAdjustable-speed drives perform many vital control functions in the industry, serving in such diverse applications as rolling mills, variable-speed compressors, fans, and pumps. When an adjustable-speed drive fails due to a bearing failure, it is usually catastrophic. Bearing defects introduce vibration anomalies that alter the current characteristic frequencies. This paper addresses the application of motor current signature analysis using wavelet packet decomposition to detect bearing faults in adjustable-speed drives.Publication Metadata only Bearing fault detection via wavelet packet decomposition with spectral post processing(IEEE, 2007) Eren, Levent; Teotrakool, Kaptan; Devaney, Michael J.; Bahcesehir University; University of Missouri System; University of Missouri ColumbiaWe present a method for detecting motor bearing fault conditions via wavelet packet decomposition (WPD) of induction motor current. This method involves the decomposition of motor current into equally spaced frequency bands by using all pass implementation of Elliptic IIR half-band filters in the filter bank structure to obtain wavelet packet coefficients (WPC). Then, the bias in WPCs for each frequency band is removed to suppress leakage from adjacent frequency bands. Fourier analysis is applied to wavelet packet coefficients to provide higher frequency resolution within each frequency band. The changes in the energy levels of frequency bands in which motor fault related current frequencies lie are monitored to detect motor fault conditions.Publication Metadata only Broken Rotor Bar Detection via Wavelet Packet Decomposition of Motor Current(PRAISE WORTHY PRIZE SRL, 2009) Eren, Levent; Cekic, Yalcin; Devaney, Michael J.; Bahcesehir University; Bahcesehir University; University of Missouri System; University of Missouri ColumbiaIn this paper, discrete wavelet packet decomposition (DWPD) of induction motor current is proposed for detecting broken rotor bar conditions. Good frequency separation is essential for accurate detection of broken rotor bars since it is difficult to separate the rotor bar frequency components from the fundamental supply frequency component at rated speeds. The proposed method provides good frequency separation at a very low computational complexity. The motor current signal is first notch filtered to suppress the power system fundamental frequency. Then, the motor current is decomposed into equally spaced frequency bands by using all-pass implementation of elliptic IIR half-band filter. Next, baseline shifting of wavelet packet coefficients (WPCs) is applied to remove the filtering distortion caused by other harmonics. Finally, energy level of each frequency band is calculated by determining rms values from WPCs of associated frequency bands. The changes in the energy levels of frequency bands in which broken rotor bar related current frequencies lie are monitored to defect motor fault conditions. Copyright (C) 2009 Praise Worthy Prize S.r.l - All rights reservedPublication Metadata only Harmonic analysis via wavelet packet decomposition using special elliptic half-band filters(IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC, 2007) Eren, Levent; Unal, Mehmet; Devaney, Michael J.; Bahcesehir University; Bahcesehir University; University of Missouri System; University of Missouri ColumbiaThe fast Fourier transform (FFT) is the most widely used power system harmonic analysis tool in real-time power metering due to its computational efficiency. Recently, an alternate method, i.e., wavelet packet decomposition (WPD), has been applied to power system signals to meter the voltage and current harmonies. Although the new method provides better analysis, the computational complexity of WPD places a limitation on its use in real-time metering. This paper proposes the use of all-pass-implemented special half-band elliptic infinite-impulse-response filters in the WPD of power system signals. The proposed implementation reduces the computational complexity to levels comparable to FFT.Publication Metadata only EFFECT OF WIND ENERGY ON TURKEY'S ENERGY SUPPLY SECURITY(GAZI UNIV, FAC ENGINEERING ARCHITECTURE, 2009) Albostan, Ayhan; Cekic, Yalcin; Eren, Levent; Bahcesehir UniversityEnergy consumption is one of the major indicators showing how developed a country is both socially and economically. Today, energy plays an important role in international relations. Fossil based energy resources are limited and they affect the environment in a negative way. Furthermore, the increase in energy demand recently resulted in price hikes in oil and natural gas prices. Increases in natural gas and oil prices have a very negative impact on the economies of developing countries such as Turkey. Therefore, it is a must for all nations to effectively use renewable energy resources which are clean and friendly to the environment. In this study, the possible contribution of renewable energy resources to Turkey's energy supply study will be studied.Publication Metadata only Enhanced Feature Detection in Bearing Health Diagnosis Using Wavelet Packet Transform with Spectral Post Processing(PRAISE WORTHY PRIZE SRL, 2009) Eren, Levent; Bahcesehir UniversityA hybrid method for detecting motor bearing fault conditions via discrete wavelet packet decomposition (DWPD) of induction motor current with spectral post processing is presented in this paper. This method involves the decomposition of motor current into equally spaced frequency bands by using an all-pass implementation of elliptic IIR half-band filters in the filter bank structure to obtain wavelet packet coefficients (WPC) in a computationally efficient way. Then, the bias in WPCs for each frequency band is removed to suppress both power system harmonics and leakage from adjacent frequency bands. Finally, Fourier analysis is applied to WPCs to provide higher frequency resolution within each frequency band The hybrid approach provides better results in early detection of bearing faults. Copyright (C) 2009 Praise Worthy Prize S.r.l. - All rights reservedPublication Metadata only Bearing Damage Detection via Wavelet Packet Decomposition of the Stator Current(2004) Eren, Levent; Devaney, Michael Joseph; Eren, Levent, Department of Electrical Engineering, Bahçeşehir Üniversitesi, Istanbul, Turkey; Devaney, Michael Joseph, College of Engineering, Columbia, United StatesBearing faults are one of the major causes of motor failures. The bearing defects induce vibration, resulting in the modulation of the stator current. In this paper, the stator current is analyzed via wavelet packet decomposition to detect bearing defects. The proposed method enables the analysis of frequency bands that can accommodate the rotational speed dependence of the bearing defect frequencies. The wavelet packet decomposition also provides a better treatment of nonstationary stator current than currently used Fourier techniques. © 2008 Elsevier B.V., All rights reserved.Publication Metadata only Neural network based motor bearing fault detection(2004) Eren, Levent; Karahoca, Adem; Devaney, Michael Joseph; Demidenko, S.; Ottoboni, R.; Petri, D.; Piuri, V.; Weng, D.C.T.; Eren, Levent, Department of Electrical Engineering, Bahçeşehir Üniversitesi, Istanbul, Turkey; Karahoca, Adem, Department of Computer Engineering, Bahçeşehir Üniversitesi, Istanbul, Turkey; Devaney, Michael Joseph, College of Engineering, Columbia, United StatesBearing faults are the biggest single cause of motor failures. The bearing defects induce vibration resulting in the modulation of the stator current. The stator current can be analyzed via wavelet packet decomposition to detect bearing defects. This method enables the analysis of frequency bands that can accommodate the rotational speed dependence of the bearing defect frequencies. In this study, Radial Basis Function Neural Networks are used to improve bearing fault detection procedure. © 2011 Elsevier B.V., All rights reserved.Publication Metadata only Harmonic analysis via wavelet packet decomposition using special elliptic half-band filters(2004) Eren, Levent; Ünal, Mehmet; Devaney, Michael Joseph; Demidenko, S.; Ottoboni, R.; Petri, D.; Piuri, V.; Weng, D.C.T.; Eren, Levent, Department of Electrical Engineering, Bahçeşehir Üniversitesi, Istanbul, Turkey; Ünal, Mehmet, Department of Mathematics, Bahçeşehir Üniversitesi, Istanbul, Turkey; Devaney, Michael Joseph, College of Engineering, Columbia, United StatesThe Fast Fourier Transform is the most widely used power system harmonic analysis tool in real time power metering due to its computational efficiency. Recently, an alternate method, wavelet packet decomposition, has been applied to power system signals to meter voltage and current harmonics. Although the new method provides better analysis, the computational complexity of wavelet packet decomposition places a limitation on its use in real time metering. This paper proposes the use of all-pass implemented half-band elliptic IIR filters in the wavelet packet decomposition of power system signals. The proposed implementation reduces the computational complexity to levels comparable to Fast Fourier Transform. © 2011 Elsevier B.V., All rights reserved.Publication Metadata only Detecting motor bearing faults: Monitoring an induction motor's current and detecting bearing failure(2004) Devaney, Michael Joseph; Eren, Levent; Devaney, Michael Joseph, University of Missouri, Columbia, United States; Eren, Levent, Bahçeşehir Üniversitesi, Istanbul, TurkeyThree-phase induction motors are widely used in the industry for heating, cooling, refrigeration, pumping, conveyors, and similar applications. However, there are instances that these motors fail due to bearing faults, insulation faults, and rotor faults. When failure occurs, the bearings need to be replaced. Although the replacement of defective bearings is the cheapest fix, it is the most diffult one to detect. Fortunately, a circuit monitor for these motors is now available. Incipient bearing failures are detectable by the presence of characteristic machine vibration frequencies associated with the various modes of bearing failure. The circuit monitor can detect these frequencies using wavelet packet decomposition and a radial basis neural network. By monitoring an induction motor's current, this device defines a bearing failure. © 2008 Elsevier B.V., All rights reserved.