349
Views
1
CrossRef citations to date
0
Altmetric
Research Article

Early fault diagnosis based on reinforcement learning optimized-SVM model with vibration-monitored signals

ORCID Icon, ORCID Icon, ORCID Icon, ORCID Icon &

References

  • Ait-Izem, T., M.-F. Harkat, M. Djeghaba, and F. Kratz. 2018. Sensor fault detection based on principal component analysis for interval-valued data. Quality Engineering 30 (4):635–47. doi:10.1080/08982112.2017.1391288.
  • Al-Dulaimi, A., A. Asif, and A. Mohammadi. 2020. Noisy parallel hybrid model of NBGRU and NCNN architectures for remaining useful life estimation. Quality Engineering 32 (3):371–87. doi:10.1080/08982112.2020.1754427.
  • Choi, H., C.-W. Kim, and D. Kwon. 2020. Data-driven fault diagnosis based on coal-fired power plant operating data. Journal of Mechanical Science and Technology 34 (10):3931–6. doi:10.1007/s12206-020-2202-0.
  • Deng, W., R. Yao, H. Zhao, X. Yang, and G. Li. 2019. A novel intelligent diagnosis method using optimal LS-SVM with improved PSO algorithm. Soft Computing 23 (7):2445–62. doi:10.1007/s00500-017-2940-9.
  • Ding, J., L. Huang, D. Xiao, and X. Li. 2020. GMPSO-VMD algorithm and its application to rolling bearing fault feature extraction. Sensors 20 (7):1946. doi:10.3390/s20071946.
  • Fatyanosa, T. N., and M. Aritsugi. 2020. Effects of the number of hyperparameters on the performance of GA-CNN. Paper presented at the 2020 IEEE. /ACM International Conference on Big Data Computing, Applications and Technologies (BDCAT).
  • Flood, M. W., and B. Grimm. 2021. EntropyHub: An open-source toolkit for entropic time series analysis. Plos One 16 (11):e0259448. doi:10.1371/journal.pone.0259448.
  • Guan, X., W. Gao, H. Peng, N. Shu, and D. W. Gao. 2022. Image-based incipient fault classification of electrical substation equipment by transfer learning of deep convolutional neural network. IEEE Canadian Journal of Electrical and Computer Engineering 45 (1):1–8. doi:10.1109/ICJECE.2021.3109293.
  • Hwang, S.-Y., K.-S. Kim, H.-J. Kim, H.-B. Jun, and J.-H. Lee. 2021. Application of PCA and classification for fault diagnosis of MAB installed in petrochemical plant process facilities. Applied Sciences 11 (9):3780. doi:10.3390/app11093780.
  • Inturi, V., G. R. Sabareesh, and P. K. Penumakala. 2020. Bearing fault severity analysis on a multi-stage gearbox subjected to fluctuating speeds. Experimental Techniques 44 (5):541–52. doi:10.1007/s40799-020-00370-z.
  • Jiaocheng, M., S. Jinan, Z. Xin, and Z. Peng. 2022. Bayes-DCGRU with bayesian optimization for rolling bearing fault diagnosis. Applied Intelligence 52 (10):11172–83. doi:10.1007/s10489-021-02924-z.
  • Kumar, M., S. Sharma, D. Chaudhary, and S. Prakash. 2021. Image recognition using artificial intelligence. Paper presented at the 2021 International Conference on Advance Computing and Innovative Technologies in Engineering (ICACITE).
  • Li, Y., S. Wang, Y. Yang, and Z. Deng. 2022. Multiscale symbolic fuzzy entropy: An entropy denoising method for weak feature extraction of rotating machinery. Mechanical Systems and Signal Processing 162:108052. doi:10.1016/j.ymssp.2021.108052.
  • Liang, P., C. Deng, J. Wu, and Z. Yang. 2020. Intelligent fault diagnosis of rotating machinery via wavelet transform, generative adversarial nets and convolutional neural network. Measurement 159:107768. doi:10.1016/j.measurement.2020.107768.
  • Lu, Y., R. Xie, and S. Y. Liang. 2020. CEEMD-assisted kernel support vector machines for bearing diagnosis. The International Journal of Advanced Manufacturing Technology 106 (7–8):3063–70. doi:10.1007/s00170-019-04858-w.
  • Lv, Y., Q. Zhou, Y. Li, and W. Li. 2021. A predictive maintenance system for multi-granularity faults based on AdaBelief-BP neural network and fuzzy decision making. Advanced Engineering Informatics 49:101318. doi:10.1016/j.aei.2021.101318.
  • Lv, Y., W. Zhao, Z. Zhao, W. Li, and K. K. Ng. 2022. Vibration signal-based early fault prognosis: Status quo and applications. Advanced Engineering Informatics 52:101609. doi:10.1016/j.aei.2022.101609.
  • Mao, W., B. Sun, and L. Wang. 2021. A new deep dual temporal domain adaptation method for online detection of bearings early fault. Entropy 23 (2):162. doi:10.3390/e23020162.
  • Mao, W., S. Tian, J. Fan, X. Liang, and A. Safian. 2020. Online detection of bearing incipient fault with semi-supervised architecture and deep feature representation. Journal of Manufacturing Systems 55:179–98. doi:10.1016/j.jmsy.2020.03.005.
  • Shalamov, V., V. Efimova, S. Muravyov, and A. Filchenkov. 2018. Reinforcement-based method for simultaneous clustering algorithm selection and its hyperparameters optimization. Procedia Computer Science 136:144–53. doi:10.1016/j.procs.2018.08.247.
  • Shao, H. D., H. K. Jiang, Y. Lin, and X. Q. Li. 2018. A novel method for intelligent fault diagnosis of rolling bearings using ensemble deep auto-encoders. Mechanical Systems and Signal Processing 102:278–97. doi:10.1016/j.ymssp.2017.09.026.
  • Shao, H., J. Lin, L. Zhang, and M. Wei. 2020. Compound fault diagnosis for a rolling bearing using adaptive DTCWPT with higher order spectra. Quality Engineering 32 (3):342–53. doi:10.1080/08982112.2020.1749654.
  • Tabrizi, A., L. Garibaldi, A. Fasana, and S. Marchesiello. 2015. Early damage detection of roller bearings using wavelet packet decomposition, ensemble empirical mode decomposition and support vector machine. Meccanica 50 (3):865–74. doi:10.1007/s11012-014-9968-z.
  • Wang, B., Y. Lei, N. Li, and N. Li. 2020. A hybrid prognostics approach for estimating remaining useful life of rolling element bearings. IEEE Transactions on Reliability 69 (1):401–12. doi:10.1109/TR.2018.2882682.
  • Wang, T., Y. He, T. Shi, and B. Li. 2019. Transformer incipient hybrid fault diagnosis based on solar-powered RFID sensor and optimized DBN approach. IEEE Access 7:74103–10. doi:10.1109/ACCESS.2019.2921108.
  • Wang, X., S. Si, and Y. Li. 2022. Hierarchical diversity entropy for the early fault diagnosis of rolling bearing. Nonlinear Dynamics 108 (2):1447–62. doi:10.1007/s11071-021-06728-1.
  • Wang, Z., H. Huang, and Y. Wang. 2021. Fault diagnosis of planetary gearbox using multi-criteria feature selection and heterogeneous ensemble learning classification. Measurement 173:108654. doi:10.1016/j.measurement.2020.108654.
  • Wu, J., M. Lin, Y. Lv, and Y. Cheng. 2022. Intelligent fault diagnosis of rolling bearings based on clustering algorithm of fast search and find of density peaks. Quality Engineering. doi:10.1080/08982112.2022.2140436.
  • Wu, J., S. Chen, and X. Liu. 2020. Efficient hyperparameter optimization through model-based reinforcement learning. Neurocomputing 409:381–93. doi:10.1016/j.neucom.2020.06.064.
  • Xu, Y., D. Zhen, J. X. Gu, K. Rabeyee, F. Chu, F. Gu, and A. D. Ball. 2021. Autocorrelated Envelopes for early fault detection of rolling bearings. Mechanical Systems and Signal Processing 146:106990. doi:10.1016/j.ymssp.2020.106990.
  • Yousefi, N., S. Tsianikas, and D. W. Coit. 2022. Dynamic maintenance model for a repairable multi-component system using deep reinforcement learning. Quality Engineering 34 (1):16–35. doi:10.1080/08982112.2021.1977950.
  • Zhang, B., S. Zhang, and W. Li. 2019. Bearing performance degradation assessment using long short-term memory recurrent network. Computers in Industry 106:14–29. doi:10.1016/j.compind.2018.12.016.
  • Zhao, W., Y. Lv, J. Xiao, and Y. Li. 2021. Fault Diagnosis of Rolling Bearings based on GA-SVM model. Paper presented at the 2021 Global Reliability and Prognostics and Health Management (PHM-Nanjing).
  • Zheng, P., W. Zhao, Y. Lv, L. Qian, and Y. Li. 2023. Health status-based predictive maintenance decision-making via LSTM and Markov decision process. Mathematics 11 (1):109. doi:10.3390/math11010109.
  • Zheng, X., G. Zhou, D. Li, and H. Ren. 2019. Application of variational mode decomposition and permutation entropy for rolling bearing fault diagnosis. The International Journal of Acoustics and Vibration 24 (2):303–11. doi:10.20855/ijav.2019.24.21325.
  • Zhou, Y., S. Yan, Y. Ren, and S. Liu. 2021. Rolling bearing fault diagnosis using transient-extracting transform and linear discriminant analysis. Measurement 178:109298. doi:10.1016/j.measurement.2021.109298.

Reprints and Corporate Permissions

Please note: Selecting permissions does not provide access to the full text of the article, please see our help page How do I view content?

To request a reprint or corporate permissions for this article, please click on the relevant link below:

Academic Permissions

Please note: Selecting permissions does not provide access to the full text of the article, please see our help page How do I view content?

Obtain permissions instantly via Rightslink by clicking on the button below:

If you are unable to obtain permissions via Rightslink, please complete and submit this Permissions form. For more information, please visit our Permissions help page.