标题:Smoothed Fisher discriminant analysis for incipient fault diagnosis
作者:Ji, Hongquan ;Wang, Youqing ;Chen, Zhiwen
作者机构:[Ji, Hongquan ;Wang, Youqing ] College of Electrical Engineering and Automation, Shandong University of Science and Technology, Qingdao, China;[Chen, 更多
会议名称:44th Annual Conference of the IEEE Industrial Electronics Society, IECON 2018
会议日期:October 20, 2018 - October 23, 2018
来源:Proceedings: IECON 2018 - 44th Annual Conference of the IEEE Industrial Electronics Society
出版年:2019
页码:5412-5417
DOI:10.1109/IECON.2018.8591217
摘要:Fisher discriminant analysis (FDA) is a widely used tool for fault diagnosis. In addition, many modifications have also been proposed recently in the literature in order to overcome certain limitations of the traditional FDA method. However, the incipient fault diagnosis problem is not well handled by traditional FDA and its variants. In this paper, through the introduction of smoothing techniques, a new method called smoothed FDA (SFDA) is proposed to enhance the fault diagnosis performance for incipient faults. Fault diagnosability analyses of the FDA and SFDA approaches are carried out and compared with each other. It is pointed out through theoretical analysis that SFDA is superior to FDA in terms of incipient fault diagnosis. Simulation studies on a continuous stirred tank reactor process are used to demonstrate the effectiveness of the SFDA method, in comparison with traditional FDA.
© 2018 IEEE.
收录类别:EI
资源类型:会议论文
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