Abstract：A new method based on near-isometric projection and support vector machine was proposed for fault diagnosis of rolling bearings. Firstly, Gaussian random projection matrix was utilized to do dimension reduction projection for the signal data to obtain the compressed data. According to the near-isometric projection property, the compressed data kept the structure of the original signals. Then the compressed domain features were extracted from the compressed data, they were taken as the input of a support vector machine to establish the fault diagnosis model of rolling bearings and realize fault diagnosis of rolling bearings. The actual measured data of rolling bearings in different faulty states were used to verify the new method. Results demonstrated the correctness and effectiveness of the proposed method.
刘畅， 伍星，刘韬,柳小勤. 基于近似等距投影和支持向量机的滚动轴承故障诊断[J]. 振动与冲击, 2018, 37(5): 234-239.
LIU Chang, WU Xin, LIU Tao, LIU Xiaoqin. Fault diagnosis of rolling bearings based on near-isometric projection and support vector machine. JOURNAL OF VIBRATION AND SHOCK, 2018, 37(5): 234-239.
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