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Research Article

Real-time bearing fault classification of induction motor using enhanced inception ResNet-V2

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Article: 2378270 | Received 01 Jan 2024, Accepted 02 Jul 2024, Published online: 11 Jul 2024

References

  • Agrawal, P., and P. Jayaswal. 2020. Diagnosis and classifications of bearing faults using artificial neural network and support vector machine. Journal of the Institution of Engineers (India): Series C 101 (1):61–23. doi:10.1007/s40032-019-00519-9.
  • Alexakos, C. T., Y. L. Karnavas, M. Drakaki, and I. A. Tziafettas. 2021. A combined short time Fourier transform and image classification transformer model for rolling element bearings fault diagnosis in electric motors. Machine Learning and Knowledge Extraction 3 (1):228–42. doi:10.3390/make3010011.
  • Amar, M., I. Gondal, and C. Wilson. 2014. Vibration spectrum imaging: A novel bearing fault classification approach. IEEE Transactions on Industrial Electronics 62 (1):494–502. doi:10.1109/TIE.2014.2327555.
  • Chen, Y., Y. Lin, X. Xu, J. Ding, C. Li, Y. Zeng, W. Liu, W. Xie, and J. Huang. 2022. Classification of lungs infected COVID-19 images based on inception-ResNet. Computer Methods and Programs in Biomedicine 225:107053. doi:10.1016/j.cmpb.2022.107053.
  • Choudhary, A., R. K. Mishra, S. Fatima, and B. K. Panigrahi. 2023. Multi-input CNN based vibro-acoustic fusion for accurate fault diagnosis of induction motor. Engineering Applications of Artificial Intelligence 120:105872. doi:10.1016/j.engappai.2023.105872.
  • Deveci, B. U., M. Celtikoglu, O. Albayrak, P. Unal, and P. Kirci. 2023. Transfer learning enabled bearing fault detection methods based on image representations of single-dimensional signals. Information Systems Frontiers 1–53. doi:10.1007/s10796-023-10371-z.
  • Dörfler, M., and E. Matusiak. 2015. Nonstationary Gabor frames-approximately dual frames and reconstruction errors. Advances in Computational Mathematics 41 (2):293–316. doi:10.1007/s10444-014-9358-z.
  • Gangsar, P., and R. Tiwari. 2020. Signal based condition monitoring techniques for fault detection and diagnosis of induction motors: A state-of-the-art review. Mechanical Systems and Signal Processing 144:106908. doi:10.1016/j.ymssp.2020.106908.
  • Glowacz, A., W. Glowacz, Z. Glowacz, and J. Kozik. 2018. Early fault diagnosis of bearing and stator faults of the single-phase induction motor using acoustic signals. Measurement 113:1–9. doi:10.1016/j.measurement.2017.08.036.
  • Grover, C., and N. Turk. 2022. A novel fault diagnostic system for rolling element bearings using deep transfer learning on bispectrum contour maps. Engineering Science and Technology, an International Journal 31:101049. doi:10.1016/j.jestch.2021.08.006.
  • Gundewar, S. K., and P. V. Kane. 2022. Bearing fault diagnosis using time segmented fourier synchrosqueezed transform images and convolution neural network. Measurement 203:111855. doi:10.1016/j.measurement.2022.111855.
  • Guo, L., Y. Lei, S. Xing, T. Yan, and N. Li. 2018. Deep convolutional transfer learning network: A new method for intelligent fault diagnosis of machines with unlabeled data. IEEE Transactions on Industrial Electronics 66 (9):7316–25. doi:10.1109/TIE.2018.2877090.
  • Hoang, D. T., and H. J. Kang. 2019. A motor current signal-based bearing fault diagnosis using deep learning and information fusion. IEEE Transactions on Instrumentation and Measurement 69 (6):3325–33. doi:10.1109/TIM.2019.2933119.
  • Jing, L., M. Zhao, P. Li, and X. Xu. 2017. A convolutional neural network based feature learning and fault diagnosis method for the condition monitoring of gearbox. Measurement 111:1–10. doi:10.1016/j.measurement.2017.07.017.
  • Li, H., J. Huang, X. Yang, J. Luo, L. Zhang, and Y. Pang. 2020. Fault diagnosis for rotating machinery using multiscale permutation entropy and convolutional neural networks. Entropy 22 (8):851. doi:10.3390/e22080851.
  • Liu, T., S. Yan, and W. Zhang. 2016. Time–frequency analysis of nonstationary vibration signals for deployable structures by using the constant-Q nonstationary gabor transform. Mechanical Systems and Signal Processing 75:228–44. doi:10.1016/j.ymssp.2015.12.015.
  • Oyedele, O., and H. Dutta. 2023. Determining the optimal number of folds to use in a K-fold cross-validation: A neural network classification experiment. Research in Mathematics 10 (1):2201015. doi:10.1080/27684830.2023.2201015.
  • Rodríguez, P. V. J., and A. Arkkio. 2008. Detection of stator winding fault in induction motor using fuzzy logic. Applied Soft Computing 8 (2):1112–20. doi:10.1016/j.asoc.2007.05.016.
  • Shao, S., R. Yan, Y. Lu, P. Wang, and R. X. Gao. 2019. DCNN-based multi-signal induction motor fault diagnosis. IEEE Transactions on Instrumentation and Measurement 69 (6):2658–69. doi:10.1109/TIM.2019.2925247.
  • Suthar, V., V. Vakharia, V. K. Patel, and M. Shah. 2022. Detection of compound faults in ball bearings using multiscale-SinGAN, heat transfer search optimization, and extreme learning machine. Machines 11 (1):29. doi:10.3390/machines11010029.
  • Szegedy, C., S. Ioffe, V. Vanhoucke, and A. Alemi. 2017. Inception-v4, inception-resnet and the impact of residual connections on learning. In Proceedings of the AAAI conference on artificial intelligence, San Fransico, California, USA. 1. Vol. 31. doi:10.1609/aaai.v31i1.11231.
  • Szegedy, C., W. Liu, Y. Jia, P. Sermanet, S. Reed, D. Anguelov, D. Erhan, V. Vanhoucke, and A. Rabinovich. 2015. Going deeper with convolutions. In Proceedings of the IEEE conference on computer vision and pattern recognition, Boston, MA, USA. 1–9. doi:10.1109/CVPR.2015.7298594.
  • Wen, L., X. Li, and L. Gao. 2020. A transfer convolutional neural network for fault diagnosis based on ResNet-50. Neural Computing & Applications 32 (10):6111–24. doi:10.1007/s00521-019-04097-w.
  • Wen, L., X. Li, L. Gao, and Y. Zhang. 2017. A new convolutional neural network-based data-driven fault diagnosis method. IEEE Transactions on Industrial Electronics 65 (7):5990–98. doi:10.1109/TIE.2017.2774777.
  • Wu, H., M. J. Triebe, and J. W. Sutherland. 2023. A transformer-based approach for novel fault detection and fault classification/diagnosis in manufacturing: A rotary system application. Journal of Manufacturing Systems 67:439–52. doi:10.1016/j.jmsy.2023.02.018.
  • Xie, T., X. Huang, and S.-K. Choi. 2021. Intelligent mechanical fault diagnosis using multisensor fusion and convolution neural network. IEEE Transactions on Industrial Informatics 18 (5):3213–23. doi:10.1109/TII.2021.3102017.
  • Xing, C., L. Ma, and X. Yang. 2016. Stacked denoise autoencoder based feature extraction and classification for hyperspectral images. Journal of Sensors 2016:1–10. doi:10.1155/2016/3632943.
  • Xiong, J., C. Li, C.-D. Wang, J. Cen, Q. Wang, and S. Wang. 2021. Application of convolutional neural network and data preprocessing by mutual dimensionless and similar gram matrix in fault diagnosis. IEEE Transactions on Industrial Informatics 18 (2):1061–71. doi:10.1109/TII.2021.3073755.
  • Xue, Y., R. Yang, X. Chen, Z. Tian, and Z. Wang. 2023. A novel local binary temporal convolutional neural network for bearing fault diagnosis. IEEE Transactions on Instrumentation and Measurement 72:1–13. doi:10.1109/TIM.2023.3298653.
  • Yu, X., Z. Liang, Y. Wang, H. Yin, X. Liu, W. Yu, and Y. Huang. 2022. A wavelet packet transform-based deep feature transfer learning method for bearing fault diagnosis under different working conditions. Measurement 201:111597. doi:10.1016/j.measurement.2022.111597.
  • Zhang, M., H. Li, S. Pan, J. Lyu, S. Ling, and S. Su. 2021. Convolutional neural networks-based lung nodule classification: A surrogate-assisted evolutionary algorithm for hyperparameter optimization. IEEE Transactions on Evolutionary Computation 25 (5):869–82. doi:10.1109/TEVC.2021.3060833.
  • Zhang, Q., and L. Deng. 2023. An intelligent fault diagnosis method of rolling bearings based on short-time Fourier transform and convolutional neural network. Journal of Failure Analysis & Prevention 23 (2):1–17. doi:10.1007/s11668-023-01616-9.
  • Zhu, Z., G. Peng, Y. Chen, and H. Gao. 2019. A convolutional neural network based on a capsule network with strong generalization for bearing fault diagnosis. Neurocomputing 323:62–75. doi:10.1016/j.neucom.2018.09.050.