@article{ayhan_trussell_chow_song_2008, title={On the use of a lower sampling rate for broken rotor bar detection with DTFT and AR-based spectrum methods}, volume={55}, ISSN={["1557-9948"]}, DOI={10.1109/TIE.2007.896522}, abstractNote={Broken rotor bars in an induction motor create asymmetries and result in abnormal amplitude of the sidebands around the fundamental supply frequency and its harmonics. Motor current signature analysis (MCSA) techniques are applied to inspect the spectrum amplitudes at the broken rotor bar specific frequencies for abnormality and to decide about broken rotor bar fault detection and diagnosis. In this paper, we have demonstrated with experimental results that the use of a lower sampling rate with a digital notch filter is feasible for MCSA in broken rotor bar detection with discrete-time Fourier transform and autoregressive-based spectrum methods. The use of the lower sampling rate does not affect the performance of the fault detection, while requiring much less computation and low cost in implementation, which would make it easier to implement in embedded systems for motor condition monitoring.}, number={3}, journal={IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS}, author={Ayhan, Bulent and Trussell, H. Joel and Chow, Mo-Yuen and Song, Myung-Hyun}, year={2008}, month={Mar}, pages={1421–1434} }
 @article{ayhan_chow_song_2006, title={Multiple discriminant analysis and neural-network-based monolith and partition fault-detection schemes for broken rotor bar in induction motors}, volume={53}, ISSN={["1557-9948"]}, DOI={10.1109/TIE.2006.878301}, abstractNote={Broken rotor bars in induction motors can be detected by monitoring any abnormality of the spectrum amplitudes at certain frequencies in the motor-current spectrum. It has been shown that these broken-rotor-bar specific frequencies are located around the fundamental stator current frequency and are termed lower and upper sideband components. Broken-rotor-bar fault-detection schemes should rely on multiple signatures in order to overcome or reduce the effect of any misinterpretation of the signatures that are obscured by factors such as measurement noises and different load conditions. Multiple discriminant analysis (MDA) and artificial neural networks (ANNs) provide appropriate environments to develop such fault-detection schemes because of their multiinput-processing capabilities. This paper describes two fault-detection schemes for a broken-rotor-bar fault detection with a multiple signature processing and demonstrates that the multiple signature processing is more efficient than a single signature processing. The first scheme, which will be named the "monolith scheme," is based on a single large-scale MDA or ANN unit representing the complete operating load-torque region of the motor, while the second scheme, which will be named the "partition scheme," consists of many small-scale MDA or ANN units, each unit representing a particular load-torque operating region. Fault-detection performance comparison between the MDA and the ANN with respect to the two schemes is investigated using the experimental data collected for a healthy and a broken-rotor-bar case. Partition scheme distributes the computational load and complexity of the large-scale single units in a monolith scheme to many smaller units, which results in the increase of the broken-rotor-bar fault-detection performance, as is confirmed with the experimental results}, number={4}, journal={IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS}, author={Ayhan, Bulent and Chow, Mo-Yuen and Song, Myung-Hyun}, year={2006}, month={Aug}, pages={1298–1308} }
 @article{ayhan_chow_song_2005, title={Multiple signature processing-based fault detection schemes for broken rotor bar in induction motors}, volume={20}, ISSN={["0885-8969"]}, DOI={10.1109/TEC.2004.842393}, abstractNote={The existence of broken rotor bars in induction motors can be detected by monitoring any abnormality of the spectrum amplitudes at certain frequencies in the motor current spectrum. It has been shown that these broken rotor bar-specific frequencies are settled around the fundamental stator current frequency and are termed lower and upper sideband components. Broken rotor bar fault detection schemes should depend on multiple signatures in order to overcome or reduce the effect of any misinterpretation of the signatures that are obscured by factors such as measurement noises and different load conditions. Multiple discriminant analysis (MDA) provides an appropriate environment to develop such fault detection schemes because of its multi-input processing capabilities. The focus of this paper is to provide a new fault detection methodology for broken rotor bar fault detection and diagnostics in terms of its multiple signature processing feature and the motor operation partitioning concept to improve the overall detection performance. This paper describes two fault detection schemes within this methodology, and demonstrates that multiple signature processing is more efficient than single signature processing. The first scheme, which will be named the "monolith scheme," is based on a single large-scale MDA unit representing the complete operating load torque region of the motor, while the second scheme, which will be named the "partition scheme," consists of many small-scale MDA units, each unit representing a particular load torque operating region.}, number={2}, journal={IEEE TRANSACTIONS ON ENERGY CONVERSION}, author={Ayhan, B and Chow, MY and Song, MH}, year={2005}, month={Jun}, pages={336–343} }