Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/88816
Title: Virtual sensing for gearbox condition monitoring based on kernel factor analysis
Authors: Wang, Jin-Jiang
Zheng, Ying-Hao
Zhang, Lai-Bin
Duan, Li-Xiang
Zhao, Rui
Keywords: DRNTU::Engineering::Electrical and electronic engineering
Gearbox Condition Monitoring
Virtual Sensing
Issue Date: 2017
Source: Wang, J.-J., Zheng, Y.-H., Zhang, L.-B., Duan, L.-X., & Zhao, R. (2017). Virtual sensing for gearbox condition monitoring based on kernel factor analysis. Petroleum Science, 14(3), 539-548. doi:10.1007/s12182-017-0163-4
Series/Report no.: Petroleum Science
Abstract: ibration 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.
URI: https://hdl.handle.net/10356/88816
http://hdl.handle.net/10220/45967
ISSN: 1672-5107
DOI: 10.1007/s12182-017-0163-4
Rights: © 2017 The Author(s). This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.
Fulltext Permission: open
Fulltext Availability: With Fulltext
Appears in Collections:EEE Journal Articles

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