Levent Eren

İzmir University of Economics · Department of Electrical and Electronics Engineering

Adjustable-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 characterist...

The 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 harmonics. Although the new method provides b...

Bearing 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 rotation...

Adjustable speed drives (ASDs) are widely used in industry for controlling electric motors in applications such as rolling mills, compressors, fans, and pumps. Condition monitoring of ASD-fed induction machines is very critical for preventing failures. Motor current signature analysis offers a non-invasive approach to assess motor condition. Applic...

Asynchronous machines are essential components that drive critical systems across industrial, trading, and residential sectors, powering heating units, pumps, and various appliances. Yet, ensuring their reliable process is paramount to prevent costly defects and maintain productivity. Notably, failures in the rolling element bearings (REB) account...

Application of domain adaptation techniques to predictive maintenance of modern electric rotating machinery (RM) has significant potential with the goal of transferring or adaptation of a fault diagnosis model developed for one machine to be generalized on new machines and/or new working conditions. The generalized nonlinear extension of convention...

Electric motors are widely used in many industrial applications on account of stability, solidity and ease of use. Mechanical bearing faults have the highest statistical occurrence percentage among all of the motor fault types. Accurate and advance detection of the bearing faults is critical to avoid unpredicted breakdowns of electric motors. Throu...

Preventive maintenance of modern electric rotating machinery (RM) is critical for ensuring reliable operation, preventing unpredicted breakdowns and avoiding costly repairs. Recently many studies investigated machine learning monitoring methods especially based on Deep Learning networks focusing mostly on detecting bearing faults; however, none of...

Preventive maintenance of modern electric rotating machinery (RM) is critical for ensuring reliable operation, preventing unpredicted breakdowns and avoiding costly repairs. Recently many studies investigated machine learning monitoring methods especially based on Deep Learning networks focusing mostly on detecting bearing faults; however, none of...

Timely and accurate bearing fault detection and diagnosis is important for reliable and safe operation of industrial systems. In thisstudy, p erformance of a generic real-time induction bearing fault diagnosis system employing compact adaptive 1DConvolutional Neural Network (CNN) classifier is extensively studied. In the literature, although many s...

Bearing faults are by far the biggest single source of motor failures. Both fast Fourier (frequency based) and wavelet (time-scale based) transforms are used commonly in analyzing raw vibration or current data to detect bearing faults. A hybrid method, Empirical Wavelet Transform (EWT), is used in this study to provide better accuracy in detecting...

The induction motor current is nonstationary by nature, and time-scale analysis techniques such as wavelet packet decomposition (WPD) are more suitable for the analysis of the stator current for broken rotor bar detection. But, WPD is very costly in terms of the computational effort when half-band FIR filter banks are used in analysis. The implemen...

This paper reports on the fundamental role played by Ground Penetrating Radar (GPR), alongside advanced processing and presentation methods, during the tunnel boring project at a Dam and Hydro–Electric Power Station. It identifies from collected GPR data such issues as incomplete grouting and the presence of karst conduits and voids and provides fu...

Bearing faults are the biggest single source of motor failures. Artificial Neural Networks (ANNs) and other decision support systems are widely used for early detection of bearing faults. The typical decision support systems require feature extraction and classification as two distinct phases. Extracting fixed features each time may require a signi...

Early detection of the motor faults is essential and Artificial Neural Networks (ANNs) are widely used for this purpose. The typical systems usually encapsulate two distinct blocks: feature extraction and classification. Such fixed and hand-crafted features may be a sub-optimal choice and require a significant computational cost that will prevent t...

Motor current signature analysis (MCSA) is capable of providing continuous monitoring of induction motors in a non-intrusive manner. Fourier based techniques have been used widely in processing of stator current but these techniques have a shortcoming in processing non-stationary signals such as the stator current. Recently, wavelet packet decompos...

Motor Current Signature Analysis (MCSA) is one of the most widely used methods in monitoring condition of induction motors. Traditionally, the stator current is preprocessed by notch filters to suppress line fundamental frequency. Then, the fast Fourier transform is utilized for the spectral analysis of the preprocessed stator current in most appli...

Motor current signature analysis (MCSA) provides a method for non-intrusive and continuous monitoring of motors in many applications. Initially, the fast Fourier transform (FFT) was the main signal processing method used in such analysis. Recently, wavelet packet decomposition (WPD) has become popular in such applications. The second approach gives...

Wavelet packet decomposition (WPD) of line current has been successfully applied in motor fault detection. Enhanced feature selection from wavelet packet coefficients (WPCs) 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...

Energy 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 hike...

In 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...

A 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 f...

This paper presents a novel method to detect bearing defects in adjustable speed drives (ASD's). The harmonics in pulse-width-modulation (PWM) input voltage waveforms and EMI noise in ASD systems make bearing fault detection more difficult. The proposed method accomplishes bearing fault detection in ASD's by combining motor current signature analys...

We 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 coeff...

Motor current signature analysis (MCSA) provides a non-intrusive way of assessing health of a motor. In this paper, we propose a rule based motor health monitoring system that is capable of detecting faults at varying operating conditions. Motor current is non-stationary due varying load conditions. Wavelet packet decomposition is utilized in this...

Three-phase induction motors are the workhorses of industry because of their widespread use. They are used extensively for heating, cooling, refrigeration, pumping, conveyors, and similar applications. They offer users simple, rugged construction, easy maintenance, and cost-effective pricing. These factors have promoted standardization and developm...

Bearing 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...

An apparatus and method for detecting motor bearing defects obtains motor current transient data during motor start-up, analyzes the motor current transient data via discrete wavelet transform, and compares the wavelet sub-bands to a baseline signature of a bearing-defect- free motor to detect bearing defects.

First Page of the Article

Bearing 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 beating defects. The proposed method enables the analysis of frequency bands that can accommodate the rotationa...

The increased sensitivity of power electronics equipment results in high susceptibility of industrial customers to short term voltage variations such as sags and swells. Many utilities are publishing power quality index values to indicate overall quality of their distribution systems. In this study, a real time Power Quality Index Meter that combin...

This paper deals with error compensation in power metering introduced by current and voltage transformers. The ratio correction factors, RCF, are determined for each harmonic level using either the equivalent circuit model or direct measurements. Fourier based metering is then utilized to compensate for the error at each harmonic level.

The fast Fourier transform is used widely in harmonic analysis for real time power metering due to its low computational complexity. 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 comm...

Preventive maintenance of induction motors plays an important role in avoiding expensive shut-downs due to motor failures. Motor Current Signature Analysis, MCSA, provides a non-intrusive way to assess the health of a machine. In this paper, the starting current transient of an induction motor is analyzed via discrete wavelet transform to detect be...

Sequence powers are a form of electrical pollution for the vast majority of rotational loads, since these powers contribute to both line and machine losses. The negative sequence power resulting in counter-torque reduces useful work. Therefore, the negative sequence should be accounted for in the power factor calculations. In this paper, power fact...

In this study, wind energy potential of Bahçe şehir Universiy Beşiktaş Campus is determined by analyzing wind speed, wind direction, humidity, temperature, and athmospheric pressure data collected in half an hour intervals by wind energy observation station over five months in 2008. Wind energy potential of monitored area is determined by performin...