Investigation on Multi-entropy and Multi-statistical Features Fusion Approach for Fault Detection in Rolling Bearing Using VMD

References

• N. W. Nirwan and H. B. Ramani, “Condition monitoring and fault detection in roller bearing used in rolling mill by acoustic emission and vibration analysis,” Mater. Today Proc., Vol. 51, pp. 344–354, 2022.
   Google Scholar
• S. M. Jawad and A. A. Jaber, “Rolling bearing fault detection based on vibration signal analysis and cumulative sum control chart,” FME Trans., Vol. 49, no. 3, p. 685, 2021.
   Web of Science ®Google Scholar
• D. Jena and S. Panigrahi, “Precise measurement of defect width in tapered roller bearing using vibration signal,” Measurement, Vol. 55, pp. 39–50, 2014.
   Web of Science ®Google Scholar
• K. Feng, K. Wang, M. Zhang, Q. Ni, and M. J. Zuo, “A diagnostic signal selection scheme for planetary gearbox vibration monitoring under non-stationary operational conditions,” Meas. Sci. Technol., Vol. 28, no. 3, p. 035003, 2017.
   Web of Science ®Google Scholar
• S. Sharma and S. K. Tiwari, “A novel feature extraction method based on weighted multi-scale fluctuation based dispersion entropy and its application to the condition monitoring of rotary machines,” Mech. Syst. Signal. Process., Vol. 171, p. 108909, 2022.
   Web of Science ®Google Scholar
• Y. Sun, S. Li, and X. Wang, “Bearing fault diagnosis based on EMD and improved Chebyshev distance in SDP image,” Measurement, Vol. 176, p. 109100, 2021.
   Web of Science ®Google Scholar
• Y. Lei, J. Lin, Z. He, and M. J. Zuo, “A review on empirical mode decomposition in fault diagnosis of rotating machinery,” Mech. Syst. Signal. Process., Vol. 35, no. 1-2, pp. 108–26, 2013.
   Web of Science ®Google Scholar
• X. Liu, X. Zhang, Z. Luan, and X. Xu, “Rolling bearing fault diagnosis based on EEMD sample entropy and PNN,” J. Eng., Vol. 2019, no. 23, pp. 8696–700, 2019.
   Web of Science ®Google Scholar
• S. Sharma, S. K. Tiwari, and S. Singh, “The rotary machine fault detection by hybrid method based on local mean decomposition and fluctuation based dispersion entropy,” Mater. Today Proc., Vol. 43, pp. 700–5, 2021.
   Google Scholar
• A. Kumar, Y. Zhou, and J. Xiang, “Optimization of VMD using kernel-based mutual information for the extraction of weak features to detect bearing defects,” Measurement, Vol. 168, p. 108402, 2021.
   Web of Science ®Google Scholar
• A. Kumar, et al., “VMD based trigonometric entropy measure: A simple and effective tool for dynamic degradation monitoring of rolling element bearing,” Meas. Sci. Technol., Vol. 33, no. 1, p. 014005, 2021.
   Web of Science ®Google Scholar
• L. Yang, Q. Hu, and S. Zhang, “Research on fault feature extraction method of rolling bearing based on improved wavelet threshold and CEEMD,” J. Phys: Conf. Ser., Vol. 1449, no. 1, p. 012003, 2020.
   Google Scholar
• D. Sandoval, U. Leturiondo, Y. Vidal, and F. Pozo, “Entropy indicators: An approach for low-speed bearing diagnosis,” Sensors, Vol. 21, no. 3, p. 849, 2021.
   PubMed Web of Science ®Google Scholar
• S. Zhao, L. Liang, G. Xu, J. Wang, and W. Zhang, “Quantitative diagnosis of a spall-like fault of a rolling element bearing by empirical mode decomposition and the approximate entropy method,” Mech. Syst. Signal. Process., Vol. 40, no. 1, pp. 154–77, 2013.
   Web of Science ®Google Scholar
• J. S. Richman and J. R. Moorman, “Physiological time-series analysis using approximate entropy and sample entropy,” Am. J. Physiol.-Heart Circ. Physiol., Vol. 278, pp. H2039–H2049, 2000.
   PubMed Web of Science ®Google Scholar
• W. Chen, Z. Wang, H. Xie, and W. Yu, “Characterization of surface EMG signal based on fuzzy entropy,” IEEE Trans. Neural Syst. Rehabil. Eng., Vol. 15, no. 2, pp. 266–72, 2007.
   PubMed Web of Science ®Google Scholar
• S. Sharma, S. Tiwari, and S. Singh, “Integrated approach based on flexible analytical wavelet transform and permutation entropy for fault detection in rotary machines,” Measurement, Vol. 169, p. 108389, 2021.
   Web of Science ®Google Scholar
• H. Azami and J. Escudero, “Improved multiscale permutation entropy for biomedical signal analysis: Interpretation and application to electroencephalogram recordings,” Biomed. Signal. Process. Control., Vol. 23, pp. 28–41, 2016.
   Web of Science ®Google Scholar
• H. Azami, M. Rostaghi, D. Abásolo, and J. Escudero, “Refined composite multiscale dispersion entropy and its application to biomedical signals,” IEEE Trans. Biomed. Eng., Vol. 64, no. 12, pp. 2872–9, 2017.
   PubMed Web of Science ®Google Scholar
• R. N. Toma, A. E. Prosvirin, and J.-M. Kim, “Bearing fault diagnosis of induction motors using a genetic algorithm and machine learning classifiers,” Sensors, Vol. 20, no. 7, p. 1884, 2020.
   PubMed Web of Science ®Google Scholar
• X. Ran, X. Zhou, M. Lei, W. Tepsan, and W. Deng, “A novel k-means clustering algorithm with a noise algorithm for capturing urban hotspots,” Appl. Sci., Vol. 11, no. 23, p. 11202, 2021.
   Google Scholar
• A. A. Singh, C. I. Harikrishnan, S. K. Tiwari, and S. Sharma. “A multi-entropy fusion approach for rolling bearing fault diagnosis integrated with variational mode decomposition,” in 2021 8th International Conference on Signal Processing and Integrated Networks (SPIN), 2021, pp. 367–72.
   Google Scholar
• H. Cui, Y. Guan, and H. Chen, “Rolling element fault diagnosis based on VMD and sensitivity MCKD,” IEEE. Access, Vol. 9, pp. 120297–308, 2021.
   Web of Science ®Google Scholar
• Q. Ni, J. C. Ji, K. Feng, and B. Halkon, “A fault information-guided variational mode decomposition (FIVMD) method for rolling element bearings diagnosis,” Mech. Syst. Signal. Process., Vol. 164, p. 108216, 2022.
   Web of Science ®Google Scholar
• G. d. N. P. Leite, A. M. Araújo, P. A. C. Rosas, T. Stosic, and B. Stosic, “Entropy measures for early detection of bearing faults,” Phys. A Stat. Mech. Appl., Vol. 514, pp. 458–72, 2019.
   Google Scholar
• S. Sharma, S. Tiwari, and S. Singh, “The rotary machine fault detection by hybrid method based on local mean decomposition and fluctuation based dispersion entropy,” Mater. Today Proc., Vol. 43, pp. 700–5, 2021.
   Google Scholar
• A. S. Minhas, P. Kankar, N. Kumar, and S. Singh, “Bearing fault detection and recognition methodology based on weighted multiscale entropy approach,” Mech. Syst. Signal. Process., Vol. 147, p. 107073, 2021.
   Google Scholar