Fault Diagnosis of Gearbox by FastICA and Residual Mutual Information Based Feature Extraction

摘要：

Independent Component Analysis (ICA) is a powerful tool for redundancy reduction and nongaussian data analysis. Artificial Neural Network (ANN), especially the Self- Organizing Map (SOM) based on unsupervised learning is a kind of excellent method for pattern clustering and recognition. By combining ICA with ANN, we proposed a novel multi-layer neural network for pattern classification. First, two neural ICA algorithms were applied to fusion of multi-channel measurements by sensors. Furthermore, a unit for further feature extraction was used to capture statistical features higher than second order, which embedded into the measurements. Second, certain typical neural classifier such as Multi-Layer Perceptron (MLP), Radial Basis Function (RBF) or SOM was trained for the final pattern classification. The contrastive experimental results of fault diagnosis using a pump dataset show that the proposed multi-layer neural network with ICA based feature extraction can classify various fault patterns at considerable accuracy, and be constructed in simpler way, both of which imply its great potential in fault diagnosis.

关键词： Independent component analysis Redundancy reduction FastICA Residual Mutual Information Feature Extraction Fault diagnosis of Gearbox

作者: Jiao Weidong

作者单位: Department of Mechanical Engineering Jiaxing University Jiaxing,Zhejiang Province 314001,China Department of Mechanical Engineering and Automation Zhejiang University Hangzhou,Zhejiang Province 310027,China

会议类型: 国际会议

会议名称: 2009 IEEE International Conference on Information and Automation(2009年 IEEE信息与自动化国际学术会议)

会议地点: 珠海、澳门

会议语种:英文

页码: 928-932

在线出版日期: 2009-06-22（万方平台首次上网日期，不代表论文的发表时间）