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- 저자 : Fucheng Sun (검색결과 2 건)
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Xin Zhang,Jianmin Zhao,Xinghui Zhang,Xianglong Ni,Haiping Li,Fucheng Sun 대한기계학회 2019 JOURNAL OF MECHANICAL SCIENCE AND TECHNOLOGY Vol.33 No.3
Gearbox compound fault pattern recognition is challenging because of its complexity and non-stationarity of the vibration signal. In this study is proposed a novel hybrid method based on narrow band interference canceller (NIC), multifractal detrended fluctuation analysis (MFDFA) and support vector machine optimized by whale optimization algorithm (WOASVM) for compound fault pattern recognition of gearbox. Specifically, the raw signal is processed by NIC to filter the deterministic signal which interferes with the fault signal, and then the multifractal features are extracted from the residual signal via MFDFA. Finally, the compound fault pattern is identified via WOASVM. Compound fault experiments of a gearbox under fixed condition and variable condition were done to evaluate the performance of the proposed method. The results show that the proposed method can effectively identify the compound faults and it outperforms other methods mentioned in this paper.
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New degradation feature extraction method of planetary gearbox based on alpha stable distribution
Wenxin Qiao,Xianglong Ni,Lei Wang,Xin Lv,Liwei Chen,Fucheng Sun 대한기계학회 2021 JOURNAL OF MECHANICAL SCIENCE AND TECHNOLOGY Vol.35 No.1
The planetary transmission system has been widely used in industry because of its various advantages. And the study on degradation feature extraction method of planetary gearbox is of major significance for mechanical system prognostics and health management (PHM). In this paper, the alpha stable distribution characteristics of planetary gearbox vibration signals in performance degradation process are verified. By observing the change of alpha stable distribution for planetary gearbox degradation experiment data, a new degradation feature extraction method based on alpha stable distribution is proposed, which is called the height of probability distribution (HPD). Through comparative analysis, it is determined that HPD has better linearity and less fluctuation compared with conventional degradation features in planetary gearbox accelerated degradation stage. Moreover, in the accelerated degradation stage, the degradation trend prediction result based on HPD is closer to the actual data than conventional degradation features no matter using Wiener-based or LSSVM-based prediction method. These conclusions indicate that the newly proposed HPD works well and gives accurate estimates for condition monitoring and degradation trend prediction of planetary gearbox.
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