Abstract
Aiming at the complex fault signal components and difficulty in identifying the fault features of a hoist spindle device, this study proposes a method based on a filtering algorithm, Hilbert-Huang transform (HHT), energy entropy, and support vector machines (SVM). The filtering method is applied to the vibration signal under different fault conditions. Then, the Hilbert-Huang transform is applied to the noise-reduced signal. The empirical mode decomposition (EMD) method decomposes the noise-reduced vibration signal into a set of intrinsic mode functions (IMF). Then, the Hilbert transform (HT) calculates the envelope spectrum of the first few IMFs. Afterward, it evaluates and extracts the fault characteristic frequencies. Finally, the identification of different fault defect types is determined by combining the intrinsic modal energy entropy and SVM. The experimental results show that the method can accurately identify the faults in the rotor bearing system and is an effective fault signal processing method.
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Acknowledgments
The authors thanked all those who helped them in writing this thesis. The research was supported by the National Key Research and Development Program (2017YFF0210604), the National Natural Science Foundation of China (No.51975572), the Top-notch Academic Programs Project of Jiangsu Higher Education Institutions (TAPP), the Project funded by the Priority Academic Program Development of Jiangsu Higher Education Institutions (PAPD).
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Jun Gu is currently pursuing the Doctorate degree in Mechatronic Engineering in China University of Mining and Technology, Xuzhou, China. His current research interests include fault diagnosis and monitoring, signal processing.
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Gu, J., Peng, Y., Lu, H. et al. Compound fault diagnosis and identification of hoist spindle device based on hilbert huang and energy entropy. J Mech Sci Technol 35, 4281–4290 (2021). https://doi.org/10.1007/s12206-021-0901-9
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DOI: https://doi.org/10.1007/s12206-021-0901-9