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      저자 : Guoan Chen (검색결과 2 건)
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      • KCI등재

        A novel energy regeneration system for emulsion pump tests

        Li Yilei,Zhu Zhencai,Chen Guoan,Cao Guohua 대한기계학회 2013 JOURNAL OF MECHANICAL SCIENCE AND TECHNOLOGY Vol.27 No.4

        A novel energy regeneration system based on cylinders and a rectifier valve for emulsion pump tests is presented and studied. The overall structure and working principles of this system are introduced. Both simulation and experiments are carried out to investigate the energy regeneration feasibility and capability of this novel system. The simulation and experimental results validate that this system is able to save energy and satisfy the test requirement. The energy recovery coefficient and overall energy regeneration coefficient of the test bench are 0.785 and 0.214, respectively. Measures to improve these two coefficients are also given accordingly after analysis of power loss. This novel system brings a new method of energy regeneration for emulsion pump tests.

      • KCI등재

        Compound fault diagnosis and identification of hoist spindle device based on hilbert huang and energy entropy

        Jun Gu,Yuxing Peng,Hao Lu,Bobo Cao,Guoan Chen 대한기계학회 2021 JOURNAL OF MECHANICAL SCIENCE AND TECHNOLOGY Vol.35 No.10

        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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