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题名: Corrosion pitting damage detection of rolling bearings using data mining techniques
作者: Zhang YL(章永来); Zhou XF(周晓锋); Shi HB(史海波); Zheng ZY(郑泽宇); Li S(李帅)
作者部门: 数字工厂研究室
关键词: machine learning, rolling bearings ; corrosion pitting ; support vector data description ; SVDD, principal component analysis ; PCA
刊名: International Journal of Modelling, Identification and Control
ISSN号: 1746-6172
出版日期: 2015
卷号: 24, 期号:3, 页码:235-243
收录类别: EI
产权排序: 1
项目资助者: National Scientific and Technological Support Projects of China under Grant No.2012BAF12B08
摘要: Detection of rolling bearings is very crucial for the reliable operation in the process of condition monitoring of rotating machinery. In this paper, a novel monitoring method using support vector data description (SVDD) with principal component analysis (PCA) for fault diagnosis of corrosion pitting on the raceways and balls in rolling bearings is proposed to improve diagnostic accuracy based on feature extraction dataset of vibration signals. The feasibility and validity of the proposed monitoring scheme are investigated through case study. Experiment results show that the proposed method can achieve 92.85% accuracy, 93.11% sensitivity, and 90.47% specificity based on an unbalanced dataset.
语种: 英语
内容类型: 期刊论文
URI标识: http://ir.sia.cn/handle/173321/17292
Appears in Collections:数字工厂研究室_期刊论文

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