Machinery Fault Feature Extraction Based on Independent Component Analysis and Correlation Coefficient
Zhao Zhihong 1,2,Yang Shaopu2, Shen Yongjun2
1. School of Computing and Informatics, Shijiazhuang Tiedao University, Shijiazhuang, 050043, China2. Key Laboratory of Traffic Safety and Control of Hebei Province, Shijiazhuang, 050043, China
Abstract：This paper proposed a machinery fault feature extraction method based on Independent Component Analysis (ICA) and correlation coefficient. First, the ICA is used for vibration signal of each fault category. The extracted independent components include the information of the fault. Then the absolute sum of the correlation coefficients of the test sample and the extracted indepent components of each category are used as the feature vetor. Finally the support vector machine is used as the classification method for fault diagnosis. The proposed fault feature extraction method is applied to two tasks: gear feault diagnosis and roller bearing fault diagnosis tasks. The ICA is applied to extracting independent features in the proposed method. Through experiments, we demonstrate that the ICA of each fault category and correlation coefficient can extract useful features for machinery fault diagnosis.
2013, Vol. 32 
