Virtual sensing for gearbox condition monitoring based on kernel factor analysis
投稿时间:2016-12-12  
英文关键词:Gearbox condition monitoring Virtual sensing Feature selection and fusion
基金项目:This research acknowledges the financial support from the National Science Foundation of China (No. 51504274 and No. 51674277), the National Key Research and Development Program of China (No. 2016YFC0802103), and the Science Foundation of China University of Petroleum, Beijing (No. 2462014YJRC039 and 2462015YQ0403).
作者单位
Jin-Jiang Wang School of Mechanical and Transportation Engineering, China University of Petroleum, Beijing 102249, China 
Ying-Hao Zheng School of Mechanical and Transportation Engineering, China University of Petroleum, Beijing 102249, China 
Lai-Bin Zhang School of Mechanical and Transportation Engineering, China University of Petroleum, Beijing 102249, China 
Li-Xiang Duan School of Mechanical and Transportation Engineering, China University of Petroleum, Beijing 102249, China 
Rui Zhao School of Electrical and Electronic Engineering, Nanyang Technological University, Singapore 639798, Singapore 
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英文摘要:
      Vibration and oil debris analysis are widely used in gearbox condition monitoring as the typical indirect and direct sensing techniques. However, they have their own advantages and disadvantages. To better utilize the sensing information and overcome its shortcomings, this paper presents a virtual sensing technique based on artificial intelligence by fusing low-cost online vibration measurements to derive a gearbox condition indictor, and its performance is comparable to the costly offline oil debris measurements. Firstly, the representative features are extracted from the noisy vibration measurements to characterize the gearbox degradation conditions. However, the extracted features of high dimensionality present nonlinearity and uncertainty in the machinery degradation process. A new nonlinear feature selection and fusion method, named kernel factor analysis, is proposed to mitigate the aforementioned challenge. Then the virtual sensing model is constructed by incorporating the fused vibration features and offline oil debris measurements based on support vector regression. The developed virtual sensing technique is experimentally evaluated in spiral bevel gear wear tests, and the results show that the developed kernel factor analysis method outperforms the state-of-the-art feature selection techniques in terms of virtual sensing model accuracy.
Jin-Jiang Wang,Ying-Hao Zheng,Lai-Bin Zhang,Li-Xiang Duan,Rui Zhao,2017.Virtual sensing for gearbox condition monitoring based on kernel factor analysis.Petroleum Science,(3):539~548.
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