Research Article
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Year 2023, Volume: 18 Issue: 1, 275 - 290, 29.03.2023
https://doi.org/10.55525/tjst.1261887

Abstract

References

  • Kumar P, Hati AS. Dilated convolutional neural network-based model for bearing faults and broken rotor bar detection in squirrel cage induction motors. Expert Syst Appl 2022;191.
  • Taher SA, Malekpour M, Farshadnia M. Diagnosis of broken rotor bars in induction motors based on harmonic analysis of fault components using modified adaptive notch filter and discrete wavelet transform, Simul Model Pract Theory 2014; 44: 26–41.
  • Mustafa MO, Nikolakopoulos G, Gustafsson T, Kominiak D. A fault detection scheme based on minimum identified uncertainty bounds violation for broken rotor bars in induction motors. Control Eng Pract 2016; 48: 63–77.
  • Halder S, Dora BK, Bhat S. An Enhanced Pathfinder Algorithm-based MCSA for rotor breakage detection of induction motor, J Comput Sci 2022;64.
  • Shi P, Chen Z, Vagapov Y, Zouaoui Z. A new diagnosis of broken rotor bar fault extent in three-phase squirrel cage induction motor, Mech Syst Signal Process 2014; 42(1–2): 388–403.
  • Aydin I, Karakose M, Akin E. A new method for early fault detection and diagnosis of broken rotor bars. Energy Convers Manag 2011; 52(4): 1790–1799.
  • Sabir H, Ouassaid M, Ngote N. An experimental method for diagnostic of incipient broken rotor bar fault in induction machines. Heliyon 2022; 8(3).
  • Bessam B, Menacer A, Boumehraz M, Cherif H. Detection of broken rotor bar faults in induction motor at low load using neural network. ISA Transactions 2016; 64: 241–246.
  • Singh G, Naikan VNA. Detection of half broken rotor bar fault in VFD driven induction motor drive using motor square current MUSIC analysis, Mech Syst Signal Process 2018; 110: 333–348.
  • Ameid T, Menacer A, Talhaoui H, Azzoug Y. Discrete wavelet transform and energy eigen value for rotor bars fault detection in variable speed field-oriented control of induction motor drive. ISA Transactions 2018; 79: 217–231.
  • Quiroz JC, Mariun N, Mehrjou MR, Izadi M, Misron N, Mohd Radzi MA. Fault detection of broken rotor bar in LS-PMSM using random forests. Measurement: Journal of the International Measurement Confederation 2018; 116: 273–280.
  • Gandhi P, Turk DN, Dahiya DR. Health monitoring of induction motors through embedded systems-simulation of broker rotor bar fault and abnormal gear teeth fault, Microprocess Microsyst 2020; 76.
  • Rangel-Magdaleno J, Peregrina-Barreto H, Ramirez-Cortes J, Cruz-VegaI. Hilbert spectrum analysis of induction motors for the detection of incipient broken rotor bars. Measurement: Journal of the International Measurement Confederation 2017; 109: 247–255.
  • Abd-el-Malek M, Abdelsalam AK, Hassan OE. Induction motor broken rotor bar fault location detection through envelope analysis of start-up current using Hilbert transform, Mech Syst Signal Process 2017; 93: 332–350.
  • Halder S, Bhat S, Dora BK. Inverse thresholding to spectrogram for the detection of broken rotor bar in induction motor. Measurement: Journal of the International Measurement Confederation 2022;198.
  • Liu D, Lu D. Off-the-grid compressive sensing for broken-rotor-bar fault detection in squirrel-cage induction motors. IFAC-PapersOnLine 2015; 28(21): 1451–1456.
  • Georgoulas G, Mustafa MO, Tsoumas IP, Antonino-Daviu JA, Climente-Alarcon V, Stylios CD, Nikolakopoulos G. Principal Component Analysis of the start-up transient and Hidden Markov Modeling for broken rotor bar fault diagnosis in asynchronous machines, Expert Syst Appl 2013; 40(17): 7024–7033.
  • Khater FMH, Abu El-Sebah MI, Osama M, Sakkoury KS. Proposed fault diagnostics of a broken rotor bar induction motor fed from PWM inverter. Journal of Electrical Systems and Information Technology 2016; 3(3): 387–397.
  • Meng L, Su Y, Kong X, Xu T, Lan X, Li Y. Intelligent fault diagnosis of gearbox based on differential continuous wavelet transform-parallel multi-block fusion residual network. Measurement: Journal of the International Measurement Confederation 2023; 206.
  • Wu J da, Chen JC. Continuous wavelet transform technique for fault signal diagnosis of internal combustion engines. NDT and E International 2006; 39(4): 304–311.
  • Liang P, Wang W, Yuan X, Liu S, Zhang L, Cheng Y. Intelligent fault diagnosis of rolling bearing based on wavelet transform and improved ResNet under noisy labels and environment. Eng Appl Artif Intell 2022;115.
  • He K, Zhang X, Ren S, Sun J. Deep Residual Learning for Image Recognition, 2015, http://arxiv.org/abs/1512.03385.
  • Kumar A, Vashishtha G, Gandhi CP, Tang H, Xiang J. Tacho-less sparse CNN to detect defects in rotor-bearing systems at varying speed, Eng Appl Artif Intell 2021;104.
  • Kang H, Zong X, Wang J, Chen H. Binary gravity search algorithm and support vector machine for forecasting and trading stock indices, International Review of Economics and Finance 2023; 84: 507–526.
  • Lyu F, Zhou H, Liu J, Zhou J, Tao B, Wang D. A buried hill fault detection method based on 3D U-SegNet and transfer learning, J Pet Sci Eng 2022; 218.
  • Aline ET, Rogério AF, Marcelo S, Narco ARM. Experimental database for detecting and diagnosing rotor broken bar in a three-phase induction motor, IEEE Dataport 2020.
  • Mathworks Online Available https://www.mathworks.com/help/predmaint/ug/broken-rotor-fault-detection-in-ac-induction-motors-using-vibration-and-electrical-signals.html. Access Date: 12 Nov 2022.
  • Turkoglu M, Aslan M, Arı A, Alçin ZM, Hanbay D. A multi-division convolutional neural network-based plant identification system, Peer J Comput Sci 2021; 7: p. e572.
  • Arı A. Multipath feature fusion for hyperspectral image classification based on hybrid 3D/2D CNN and squeeze-excitation network, Earth Sci Informatics 2023; 16: 1–17.
  • Donuk K, Arı A, Hanbay D. A CNN based real-time eye tracker for web mining applications, Multimed Tools Appl 2022; 81: 1–18.
  • Demir F, Sengur A, Ari A, Siddique K, Alswaitti M. Feature Mapping and Deep Long Short Term Memory Network-Based Efficient Approach for Parkinson’s Disease Diagnosis,” IEEE Access, 2021; 9: 149456–149464.

Deep Transfer Learning-Based Broken Rotor Fault Diagnosis For Induction Motors

Year 2023, Volume: 18 Issue: 1, 275 - 290, 29.03.2023
https://doi.org/10.55525/tjst.1261887

Abstract

Due to their starting and running torque needs as well as their four-quadrant operation, modern industrial drives utilise induction motors (IM). Failures in the rotor bars of the motor can be found using the voltages and currents of each of the three phases as well as the acceleration and velocity signals. For the diagnosis of the quantity of broken rotor bars for a failed IM, conventional signal processing-based feature extraction techniques and machine learning algorithms have been applied in the past. The number of broken rotor bars is determined in this study by looking into a novel technique. For the aforementioned aims, specifically, the deep learning methodologies are studied. In order to do this, convolutional neural network (CNN) transfer learning algorithms are described. Initially, a bandpass filter is used for denoising, and then the signals are transformed using the continuous wavelet transform to create time-frequency pictures (CWT). The collected images are used for deep feature extraction and classification using the support vector machine (SVM) classifier, as well as for fine-tuning the pre-trained ResNet18 model. Metrics for performance evaluation employ categorization accuracy. Additionally, the results demonstrate that the deep features that are recovered from the mechanical vibration signal and current signal yield the greatest accuracy score of 100%. Nonetheless, a performance comparison with the publicly available techniques is also done. The comparisons also demonstrate that the proposed strategy outperforms the compared methods in terms of accuracy scores.

References

  • Kumar P, Hati AS. Dilated convolutional neural network-based model for bearing faults and broken rotor bar detection in squirrel cage induction motors. Expert Syst Appl 2022;191.
  • Taher SA, Malekpour M, Farshadnia M. Diagnosis of broken rotor bars in induction motors based on harmonic analysis of fault components using modified adaptive notch filter and discrete wavelet transform, Simul Model Pract Theory 2014; 44: 26–41.
  • Mustafa MO, Nikolakopoulos G, Gustafsson T, Kominiak D. A fault detection scheme based on minimum identified uncertainty bounds violation for broken rotor bars in induction motors. Control Eng Pract 2016; 48: 63–77.
  • Halder S, Dora BK, Bhat S. An Enhanced Pathfinder Algorithm-based MCSA for rotor breakage detection of induction motor, J Comput Sci 2022;64.
  • Shi P, Chen Z, Vagapov Y, Zouaoui Z. A new diagnosis of broken rotor bar fault extent in three-phase squirrel cage induction motor, Mech Syst Signal Process 2014; 42(1–2): 388–403.
  • Aydin I, Karakose M, Akin E. A new method for early fault detection and diagnosis of broken rotor bars. Energy Convers Manag 2011; 52(4): 1790–1799.
  • Sabir H, Ouassaid M, Ngote N. An experimental method for diagnostic of incipient broken rotor bar fault in induction machines. Heliyon 2022; 8(3).
  • Bessam B, Menacer A, Boumehraz M, Cherif H. Detection of broken rotor bar faults in induction motor at low load using neural network. ISA Transactions 2016; 64: 241–246.
  • Singh G, Naikan VNA. Detection of half broken rotor bar fault in VFD driven induction motor drive using motor square current MUSIC analysis, Mech Syst Signal Process 2018; 110: 333–348.
  • Ameid T, Menacer A, Talhaoui H, Azzoug Y. Discrete wavelet transform and energy eigen value for rotor bars fault detection in variable speed field-oriented control of induction motor drive. ISA Transactions 2018; 79: 217–231.
  • Quiroz JC, Mariun N, Mehrjou MR, Izadi M, Misron N, Mohd Radzi MA. Fault detection of broken rotor bar in LS-PMSM using random forests. Measurement: Journal of the International Measurement Confederation 2018; 116: 273–280.
  • Gandhi P, Turk DN, Dahiya DR. Health monitoring of induction motors through embedded systems-simulation of broker rotor bar fault and abnormal gear teeth fault, Microprocess Microsyst 2020; 76.
  • Rangel-Magdaleno J, Peregrina-Barreto H, Ramirez-Cortes J, Cruz-VegaI. Hilbert spectrum analysis of induction motors for the detection of incipient broken rotor bars. Measurement: Journal of the International Measurement Confederation 2017; 109: 247–255.
  • Abd-el-Malek M, Abdelsalam AK, Hassan OE. Induction motor broken rotor bar fault location detection through envelope analysis of start-up current using Hilbert transform, Mech Syst Signal Process 2017; 93: 332–350.
  • Halder S, Bhat S, Dora BK. Inverse thresholding to spectrogram for the detection of broken rotor bar in induction motor. Measurement: Journal of the International Measurement Confederation 2022;198.
  • Liu D, Lu D. Off-the-grid compressive sensing for broken-rotor-bar fault detection in squirrel-cage induction motors. IFAC-PapersOnLine 2015; 28(21): 1451–1456.
  • Georgoulas G, Mustafa MO, Tsoumas IP, Antonino-Daviu JA, Climente-Alarcon V, Stylios CD, Nikolakopoulos G. Principal Component Analysis of the start-up transient and Hidden Markov Modeling for broken rotor bar fault diagnosis in asynchronous machines, Expert Syst Appl 2013; 40(17): 7024–7033.
  • Khater FMH, Abu El-Sebah MI, Osama M, Sakkoury KS. Proposed fault diagnostics of a broken rotor bar induction motor fed from PWM inverter. Journal of Electrical Systems and Information Technology 2016; 3(3): 387–397.
  • Meng L, Su Y, Kong X, Xu T, Lan X, Li Y. Intelligent fault diagnosis of gearbox based on differential continuous wavelet transform-parallel multi-block fusion residual network. Measurement: Journal of the International Measurement Confederation 2023; 206.
  • Wu J da, Chen JC. Continuous wavelet transform technique for fault signal diagnosis of internal combustion engines. NDT and E International 2006; 39(4): 304–311.
  • Liang P, Wang W, Yuan X, Liu S, Zhang L, Cheng Y. Intelligent fault diagnosis of rolling bearing based on wavelet transform and improved ResNet under noisy labels and environment. Eng Appl Artif Intell 2022;115.
  • He K, Zhang X, Ren S, Sun J. Deep Residual Learning for Image Recognition, 2015, http://arxiv.org/abs/1512.03385.
  • Kumar A, Vashishtha G, Gandhi CP, Tang H, Xiang J. Tacho-less sparse CNN to detect defects in rotor-bearing systems at varying speed, Eng Appl Artif Intell 2021;104.
  • Kang H, Zong X, Wang J, Chen H. Binary gravity search algorithm and support vector machine for forecasting and trading stock indices, International Review of Economics and Finance 2023; 84: 507–526.
  • Lyu F, Zhou H, Liu J, Zhou J, Tao B, Wang D. A buried hill fault detection method based on 3D U-SegNet and transfer learning, J Pet Sci Eng 2022; 218.
  • Aline ET, Rogério AF, Marcelo S, Narco ARM. Experimental database for detecting and diagnosing rotor broken bar in a three-phase induction motor, IEEE Dataport 2020.
  • Mathworks Online Available https://www.mathworks.com/help/predmaint/ug/broken-rotor-fault-detection-in-ac-induction-motors-using-vibration-and-electrical-signals.html. Access Date: 12 Nov 2022.
  • Turkoglu M, Aslan M, Arı A, Alçin ZM, Hanbay D. A multi-division convolutional neural network-based plant identification system, Peer J Comput Sci 2021; 7: p. e572.
  • Arı A. Multipath feature fusion for hyperspectral image classification based on hybrid 3D/2D CNN and squeeze-excitation network, Earth Sci Informatics 2023; 16: 1–17.
  • Donuk K, Arı A, Hanbay D. A CNN based real-time eye tracker for web mining applications, Multimed Tools Appl 2022; 81: 1–18.
  • Demir F, Sengur A, Ari A, Siddique K, Alswaitti M. Feature Mapping and Deep Long Short Term Memory Network-Based Efficient Approach for Parkinson’s Disease Diagnosis,” IEEE Access, 2021; 9: 149456–149464.
There are 31 citations in total.

Details

Primary Language English
Subjects Engineering
Journal Section TJST
Authors

Fırat Dişli 0000-0003-0016-3558

Mehmet Gedikpınar 0000-0002-1045-7384

Abdulkadir Sengur 0000-0003-1614-2639

Publication Date March 29, 2023
Submission Date March 8, 2023
Published in Issue Year 2023 Volume: 18 Issue: 1

Cite

APA Dişli, F., Gedikpınar, M., & Sengur, A. (2023). Deep Transfer Learning-Based Broken Rotor Fault Diagnosis For Induction Motors. Turkish Journal of Science and Technology, 18(1), 275-290. https://doi.org/10.55525/tjst.1261887
AMA Dişli F, Gedikpınar M, Sengur A. Deep Transfer Learning-Based Broken Rotor Fault Diagnosis For Induction Motors. TJST. March 2023;18(1):275-290. doi:10.55525/tjst.1261887
Chicago Dişli, Fırat, Mehmet Gedikpınar, and Abdulkadir Sengur. “Deep Transfer Learning-Based Broken Rotor Fault Diagnosis For Induction Motors”. Turkish Journal of Science and Technology 18, no. 1 (March 2023): 275-90. https://doi.org/10.55525/tjst.1261887.
EndNote Dişli F, Gedikpınar M, Sengur A (March 1, 2023) Deep Transfer Learning-Based Broken Rotor Fault Diagnosis For Induction Motors. Turkish Journal of Science and Technology 18 1 275–290.
IEEE F. Dişli, M. Gedikpınar, and A. Sengur, “Deep Transfer Learning-Based Broken Rotor Fault Diagnosis For Induction Motors”, TJST, vol. 18, no. 1, pp. 275–290, 2023, doi: 10.55525/tjst.1261887.
ISNAD Dişli, Fırat et al. “Deep Transfer Learning-Based Broken Rotor Fault Diagnosis For Induction Motors”. Turkish Journal of Science and Technology 18/1 (March 2023), 275-290. https://doi.org/10.55525/tjst.1261887.
JAMA Dişli F, Gedikpınar M, Sengur A. Deep Transfer Learning-Based Broken Rotor Fault Diagnosis For Induction Motors. TJST. 2023;18:275–290.
MLA Dişli, Fırat et al. “Deep Transfer Learning-Based Broken Rotor Fault Diagnosis For Induction Motors”. Turkish Journal of Science and Technology, vol. 18, no. 1, 2023, pp. 275-90, doi:10.55525/tjst.1261887.
Vancouver Dişli F, Gedikpınar M, Sengur A. Deep Transfer Learning-Based Broken Rotor Fault Diagnosis For Induction Motors. TJST. 2023;18(1):275-90.