Fault diagnosis of rotor systems Using ICA Based Feature Extraction

摘要：

A method is proposed for fault diagnosis of rotor systems, with independent component analysis (ICA) based feature extraction and multi-layer perceptron (MLP) based pattern classification. By the use of ICA, feature vectors are integratedly extracted from multichannel vibration measurements collected under different operating patterns (in term of rotating speed and/or load). Thus, a robust multi-MLP classifier insensitive to the change of operation conditions is constructed. Experimental results indicate invariable fault features embedded in vibration observations can be effectively captured and different fault patterns (for example imbalance, impact and loose foundation) can be correctly classified, both of which imply great potential of the proposed ICA-MLP classifier in fault diagnosis of rotor systems.

关键词： Mutual information (MI) feature extraction pattern classification principal component analysis (PCA) independent component analysis (ICA) multi-layer perceptron (MLP)

作者: Weidong Jiao Yongping Chang

作者单位: Department of Mechanical Engineering,Jiaxing University,Jiaxing,314001,China Department of Mechanica Department of Mechanical Engineering,Jiaxing University,Jiaxing,314001,China

会议类型: 国际会议

会议名称: 2009 IEEE International Conference on Robotics and Biomimetics(2009 IEEE 机器人与仿生技术国际会议 ROBIO 2009)

会议地点: 桂林

会议语种:英文

页码: 1286-1291

在线出版日期: 2009-12-19（万方平台首次上网日期，不代表论文的发表时间）