Multisensor Data Fusion Based on Independent Component Analysis for Fault Diagnosis of Rotor

机译：基于独立分量分析的多传感器数据融合在转子故障诊断中的应用

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Independent Component Analysis is applied to multi-channel vibration measurements to fuse the information of several sensors, and provide a robust and reliable fault diagnosis routine. Independent components are obtained from the measurement data set with FastICA algorithm, and their AR modeling estimates are calculated with BURG method. A probabilistic neural network is applied to the AR modeling parameters to perform the fault classification. Similar classification is applied directly to vibration measurements. Based on the results with real measurement data from the rotor test rig, it is shown that data fusion with ICA enhances the fault diagnostics routine.

机译：独立组件分析应用于多通道振动测量，以融合多个传感器的信息，并提供强大而可靠的故障诊断程序。利用FastICA算法从测量数据集中获得独立的分量，并使用BURG方法计算其AR建模估计。将概率神经网络应用于AR建模参数以执行故障分类。类似的分类直接应用于振动测量。根据转子测试台提供的真实测量数据的结果，表明与ICA进行数据融合可以增强故障诊断程序。

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来源
《International Symposium on Neural Networks pt.1; 20040819-20040821; Dalian; CN》|2004年|P.744-749|共6页
会议地点 Dalian(CN);Dalian(CN)
作者
Xiaojiang Ma; Zhihua Hao;
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Institute of Vibration Engineering, Dalian University of Technology, Dalian, 116023, P.R. China;

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原文格式 PDF
正文语种 eng
中图分类计算机网络;
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Multisensor Data Fusion Based on Independent Component Analysis for Fault Diagnosis of Rotor

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