Intelligent fault diagnosis of rolling bearings based on clustering algorithm of fast search and find of density peaks

Reference

• Ali, J. B., L. Saidi, A. Mouelhi, B. Chebel-Morello, and F. Fnaiech. 2015. Linear feature selection and classification using PNN and SFAM neural networks for a nearly online diagnosis of bearing naturally progressing degradations. Engineering Applications of Artificial Intelligence 42:67–81.
   Web of Science ®Google Scholar
• Cao, X., T. Li, and Q. Wang. 2019. RFID-based multi-attribute logistics information processing and anomaly mining in production logistics. International Journal of Production Research 57 (17):5453–66. doi: 10.1080/00207543.2018.1526421.
   Web of Science ®Google Scholar
• Cerrada, M., R.-V. Sánchez, C. Li, F. Pacheco, D. Cabrera, J. V. Oliveira, and R. E. Vásquez. 2018. A review on data-driven fault severity assessment in rolling bearings. Mechanical Systems and Signal Processing 99:169–96. doi: 10.1016/j.ymssp.2017.06.012.
   Web of Science ®Google Scholar
• Chen, Z., J. Wu, C. Deng, C. Wang, and Y. Wang. 2022. Residual deep subdomain adaptation network: A new method for intelligent fault diagnosis of bearings across multiple domains. Mechanism and Machine Theory 169:104635. doi: 10.1016/j.mechmachtheory.2021.104635.
   Web of Science ®Google Scholar
• Cheng, Y., K. Hu, J. Wu, H. Zhu, and X. Shao. 2022. Autoencoder quasi-recurrent neural networks for remaining useful life prediction of engineering systems. IEEE/ASME Transactions on Mechatronics 27 (2):1081–92. doi: 10.1109/TMECH.2021.3079729.
   Web of Science ®Google Scholar
• Cheng, Y., C. Wang, J. Wu, H. Zhu, and C. K. M. Lee. 2022. Multi-dimensional recurrent neural network for remaining useful life prediction under variable operating conditions and multiple fault modes. Applied Soft Computing 118:108507. doi: 10.1016/j.asoc.2022.108507.
   Web of Science ®Google Scholar
• Cheng, Y., H. Zhu, J. Wu, and X. Shao. 2019. Machine health monitoring using adaptive kernel spectral clustering and deep long short-term memory recurrent neural networks. IEEE Transactions on Industrial Informatics 15 (2):987–97. doi: 10.1109/TII.2018.2866549.
   Web of Science ®Google Scholar
• Du, W., J. Tao, Y. Li, and C. Liu. 2014. Wavelet leaders multifractal features based fault diagnosis of rotating mechanism. Mechanical Systems and Signal Processing 43 (1-2):57–75. doi: 10.1016/j.ymssp.2013.09.003.
   Web of Science ®Google Scholar
• Heo, S, and J. H. Lee. 2018. Fault detection and classification using artificial neural networks. IFAC-Papers 51 (18):470–5. doi: 10.1016/j.ifacol.2018.09.380.
   Google Scholar
• Hu, H., B. Tang, X. Gong, W. Wei, and H. Wang. 2017. Intelligent fault diagnosis of the high-speed train with big data based on deep neural networks. IEEE Transactions on Industrial Informatics 13 (4):2106–16. doi: 10.1109/TII.2017.2683528.
   Web of Science ®Google Scholar
• Jain, A, and D. Zongker. 1997. Feature selection: Evaluation, application, and small sample performance. IEEE Transactions on Pattern Analysis and Machine Intelligence 19 (2):153–8. doi: 10.1109/34.574797.
   Web of Science ®Google Scholar
• Jia, F., Y. Lei, J. Lin, X. Zhou, and N. Lu. 2016. Deep neural networks: A promising tool for fault characteristic mining and intelligent diagnosis of rotating machinery with massive data. Mechanical Systems and Signal Processing 72-73:303–15. doi: 10.1016/j.ymssp.2015.10.025.
   Web of Science ®Google Scholar
• Jin, X., M. Zhao, T. W. Chow, and M. Pecht. 2014. Motor bearing fault diagnosis using trace ratio linear discriminant analysis. IEEE Transactions on Industrial Electronics 61 (5):2441–51. doi: 10.1109/TIE.2013.2273471.
   Web of Science ®Google Scholar
• Li, C., L. Ledo, M. Delgado, M. Cerrada, F. Pacheco, D. Cabrera, R.-V. Sánchez, and J. Valente de Oliveira. 2017. A Bayesian approach to consequent parameter estimation in probabilistic fuzzy systems and its application to bearing fault classification. Knowledge-Based Systems 129:39–60. doi: 10.1016/j.knosys.2017.05.007.
   Web of Science ®Google Scholar
• Li, W., S. Zhang, and G. He. 2013. Semisupervised distance-preserving selforganizing map for machine-defect detection and classification. IEEE Transactions on Instrumentation and Measurement 62 (5):869–79. doi: 10.1109/TIM.2013.2245180.
   Web of Science ®Google Scholar
• Liang, P., C. Deng, J. Wu, Z. Yang, J. Zhu, and Z. Zhang. 2019. Compound fault diagnosis of gearboxes via multi-label convolutional neural network and wavelet transform. Computers in Industry 113:103132. doi: 10.1016/j.compind.2019.103132.
   Web of Science ®Google Scholar
• Loparo, K. 2012. Case Western Reserve University Bearing Data Centre Website. http://csegroups.case.edu/bearingdatacenter/home,
   Google Scholar
• Lu, S., J. Wang, and Y. Xue. 2016. Study on multi-fractal fault diagnosis based on EMD fusion in hydraulic engineering. Applied Thermal Engineering 103:798–806. doi: 10.1016/j.applthermaleng.2016.04.036.
   Web of Science ®Google Scholar
• 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.
   Web of Science ®Google Scholar
• 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.
   Web of Science ®Google Scholar
• Qian, L., Q. Pan, Y. Lv, and X. Zhao. 2022. Fault detection of bearing by resnet classifier with model-based data augmentation. Machines 10 (7):521. doi: 10.3390/machines10070521.
   Web of Science ®Google Scholar
• Ren, L., W. Lv, S. Jiang, and Y. Xiao. 2016. Fault diagnosis using a joint model based on sparse representation and SVM. IEEE Transactions on Instrumentation and Measurement 65 (10):2313–20. doi: 10.1109/TIM.2016.2575318.
   Web of Science ®Google Scholar
• Rodriguez, A, and A. Laio. 2014. Clustering by fast search and find of density peaks. Science (New York, N.Y.) 344 (6191):1492–6.
   PubMed Web of Science ®Google Scholar
• Saidi, L., J. B. Ali, and F. Fnaiech. 2015. Application of higher order spectral features and support vector machines for bearing faults classification. ISA Transactions 54:193–206. doi: 10.1016/j.isatra.2014.08.007.
   PubMed Web of Science ®Google Scholar
• Suo, M., B. Zhu, R. An, H. Sun, S. Xu, and Z. Yu. 2019. Data-driven fault diagnosis of satellite power system using fuzzy Bayes risk and SVM. Aerospace Science and Technology 84:1092–105. doi: 10.1016/j.ast.2018.11.049.
   Web of Science ®Google Scholar
• Tu, L., Y. Lv, Y. Zhang, and X. Cao. 2021. Logistics service provider selection decision making for healthcare industry based on a novel weighted density-based hierarchical clustering. Advanced Engineering Informatics 48:101301. doi: 10.1016/j.aei.2021.101301.
   Web of Science ®Google Scholar
• Wang, C., M. Gan, and C. A. Zhu. 2015. Non-negative EMD manifold for feature extraction in machinery fault diagnosis. Measurement 70:188–202. doi: 10.1016/j.measurement.2015.04.006.
   Web of Science ®Google Scholar
• Wang, T., Q. Wu, J. Zhang, B. Wu, and Y. Wang. 2020. Autonomous decision-making scheme for multi-ship collision avoidance with iterative observation and inference. Ocean Engineering 197:106873. doi: 10.1016/j.oceaneng.2019.106873.
   Web of Science ®Google Scholar
• Wang, Y., C. Deng, J. Wu, and Y. Xiong. 2015. Failure time prediction for mechanical device based on the degradation sequence. Journal of Intelligent Manufacturing 26 (6):1181–99. doi: 10.1007/s10845-013-0849-4.
   Web of Science ®Google Scholar
• Wen, L., L. Gao, X. Li, and B. Zeng. 2021. Convolutional neural network with automatic learning rate scheduler for fault classification. IEEE Transactions on Instrumentation and Measurement 70:1–12. doi: 10.1109/TIM.2020.3048792.
   PubMed Web of Science ®Google Scholar
• Wen, L., X. Li, and L. Gao. 2021. A new reinforcement learning based learning rate scheduler for convolutional neural network in fault classification. IEEE Transactions on Industrial Electronics 68 (12):12890–900. doi: 10.1109/TIE.2020.3044808.
   Web of Science ®Google Scholar
• Wu, J., C. Wu, S. Cao, S. W. Or, C. Deng, and X. Shao. 2019. Degradation data-driven time-to-failure prognostics approach for rolling element bearings in electrical machines. IEEE Transactions on Industrial Electronics 66 (1):529–39. doi: 10.1109/TIE.2018.2811366.
   Web of Science ®Google Scholar
• Wu, Z., W. Zhao, and Y. Lv. 2022. An ensemble LSTM-based AQI forecasting model with decomposition-reconstruction technique via CEEMDAN and fuzzy entropy. Air Quality, Atmosphere & Health 1–13. doi: 10.1007/s11869-022-01252-6.
   PubMed Web of Science ®Google Scholar
• Xia, M., T. Li, L. Xu, L. Liu, and C. W. de Silva. 2018. Fault diagnosis for rotating machinery using multiple sensors and convolutional neural networks. IEEE/ASME Transactions on Mechatronics 23 (1):101–10. doi: 10.1109/TMECH.2017.2728371.
   Web of Science ®Google Scholar
• Yan, X, and M. Jia. 2018. A novel optimized SVM classification algorithm with multi-domain feature and its application to fault diagnosis of rolling bearing. Neurocomputing 313:47–64. doi: 10.1016/j.neucom.2018.05.002.
   Web of Science ®Google Scholar
• Zhang, C., H. Zhu, J. Wu, Y. Cheng, Y. Deng, and C. Liu. 2018. An economical optimization model of non-periodic maintenance decision for deteriorating system. IEEE Access 6:55149–61. doi: 10.1109/ACCESS.2018.2872348.
   Web of Science ®Google Scholar
• Zhang, R., H. Tao, L. Wu, and Y. Guan. 2017. Transfer learning with neural networks for bearing fault diagnosis in changing working conditions. IEEE Access 5:14347–57. doi: 10.1109/ACCESS.2017.2720965.
   Web of Science ®Google Scholar
• Zhong, M., C. Liu, D. Zhou, W. Li, and T. Xue. 2019. Probability analysis of fault diagnosis performance for satellite attitude control systems. IEEE Transactions on Industrial Informatics 15 (11):5867–76. doi: 10.1109/TII.2019.2907382.
   Web of Science ®Google Scholar