Bearing fault diagnosis with cascaded space projection and a CNN
Yunji Zhao1
Menglin Zhou1
Li Wang2
Xiaozhuo Xu1
Nannan Zhang1
1.School of Electrical Engineering and Automation,Henan Polytechnic University,Jiaozuo 454003,Henan,China;Henan Key Laboratory of Intelligent Detection and Control of Coal Mine Equipment, Henan Polytechnic University,Jiaozuo 454003, Henan, China2.School of Electrical Engineering and Automation,Henan Polytechnic University,Jiaozuo 454003,Henan,China
摘要：Fault diagnosis is essential for the normal and safe operation of dynamic systems. To improve the spatial resolution among multiple channels and the discriminability among categories of the original data collected from actual operating equipments and to further achieve high diagnostic accuracy, this paper proposes a method for fault diagnosis by cascaded space projection (CSP) and a convolutional neural network (CNN) model. First, one of every kind of sample is selected from the original data to calculate the PCA transformation matrices. Second, the original data are expanded to 10 dimensions by the W2C projection matrix provided by Google-image searching, which is the main part of CSP. Third, the ten-dimensional matrix is multiplied by the PCA transformation matrix, which corresponds to its fault type, to make the data more representative by reducing unnecessary dimensions. Finally, the processed data are converted into images to input into a CNN, the backbone structure for fault diagnosis. To verify the effectiveness and reliability of the proposed method, the Case Western Reserve University (CWRU) and Xi'an Jiaotong University (XJTU-SY) rolling bearing datasets are used to perform experiments. Comparison with other methods is carried out to show the superiority of the proposed method. The experimental results demonstrate that the method proposed in this paper can effectively achieve 100％accuracy.
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论文发表日期：2022-02-05
在线出版日期：2025-08-15（本平台首次上网日期，不代表文献的发表时间）
页数：11( 103-113 )
英文信息