Fractal dimension-based approach for detection of multiple combined faults on induction motors
作者： Juan P Amezquita-Sanchez , Martin Valtierra-Rodriguez , David Camarena-Martinez , David Granados-Lieberman , Rene J Romero-Troncoso
关键词: Key (cryptography) 、 Fractal dimension 、 Process (computing) 、 Vibration 、 Control theory 、 Induction motor 、 Field-programmable gate array 、 Task (computing) 、 Artificial neural network 、 Computer science
摘要: Induction motors, key elements for industry, are susceptible to one or more faults at the same time; yet, they can keep working without affecting process, but increasing production costs. For this reason, a monitoring system that efficiently diagnose induction motor condition, even under multiple combined faults, is demanding task. In work, methodology and its implementation into field programmable gate array an online real-time of presented. First, fractal dimension approach, using Katz algorithm, introduced as measure variation 3-axis startup vibration signals considering these describe changes on dynamic characteristics due different faults. Then, artificial neural network determines in automatic way condition according values. The obtained results show higher overall efficiency than previous ...
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sci-hub.se 参考文章(28)
Hui Li, Yong Huang, Jinping Ou, Yuequan Bao, Fractal Dimension‐Based Damage Detection Method for Beams with a Uniform Cross‐Section Computer-aided Civil and Infrastructure Engineering. ,vol. 26, pp. 190- 206 ,(2011) , 10.1111/J.1467-8667.2010.00686.X
Zhenyu He, Xinge You, Long Zhou, Yiuming Cheung, Jianwei Du, Writer identification using fractal dimension of wavelet subbands in gabor domain Computer-Aided Engineering. ,vol. 17, pp. 157- 165 ,(2010) , 10.3233/ICA-2010-0338
David Camarena-Martinez, Martin Valtierra-Rodriguez, Arturo Garcia-Perez, Roque Alfredo Osornio-Rios, Rene de Jesus Romero-Troncoso, Empirical mode decomposition and neural networks on FPGA for fault diagnosis in induction motors. The Scientific World Journal. ,vol. 2014, pp. 908140- 908140 ,(2014) , 10.1155/2014/908140
Jaroslaw Kurek, Stanislaw Osowski, Support vector machine for fault diagnosis of the broken rotor bars of squirrel-cage induction motor Neural Computing and Applications. ,vol. 19, pp. 557- 564 ,(2010) , 10.1007/S00521-009-0316-5
J. Antonino-Daviu, P. Jover Rodriguez, M. Riera-Guasp, M. Pineda-Sánchez, A. Arkkio, Detection of combined faults in induction machines with stator parallel branches through the DWT of the startup current Mechanical Systems and Signal Processing. ,vol. 23, pp. 2336- 2351 ,(2009) , 10.1016/J.YMSSP.2009.02.007
Wenping Cao, B. C. Mecrow, G. J. Atkinson, J. W. Bennett, D. J. Atkinson, Overview of Electric Motor Technologies Used for More Electric Aircraft (MEA) IEEE Transactions on Industrial Electronics. ,vol. 59, pp. 3523- 3531 ,(2012) , 10.1109/TIE.2011.2165453
Mehran Ahmadlou, Hojjat Adeli, Anahita Adeli, Fractality and a wavelet-chaos-methodology for EEG-based diagnosis of Alzheimer disease. Alzheimer Disease & Associated Disorders. ,vol. 25, pp. 85- 92 ,(2011) , 10.1097/WAD.0B013E3181ED1160
January Gnitecki, Zahra Moussavi, The fractality of lung sounds: A comparison of three waveform fractal dimension algorithms Chaos, Solitons & Fractals. ,vol. 26, pp. 1065- 1072 ,(2005) , 10.1016/J.CHAOS.2005.02.018
PK Kankar, Satish C Sharma, SP Harsha, Rolling element bearing fault diagnosis using autocorrelation and continuous wavelet transform Journal of Vibration and Control. ,vol. 17, pp. 2081- 2094 ,(2011) , 10.1177/1077546310395970
E. Cabal-Yepez, M. Valtierra-Rodriguez, R.J. Romero-Troncoso, A. Garcia-Perez, R.A. Osornio-Rios, H. Miranda-Vidales, R. Alvarez-Salas, FPGA-based entropy neural processor for online detection of multiple combined faults on induction motors Mechanical Systems and Signal Processing. ,vol. 30, pp. 123- 130 ,(2012) , 10.1016/J.YMSSP.2012.01.021