1.School of Mechanical Engineering,Anhui University of Technology,Ma’anshan 243032,China;
2.Institute of Industrial Robots,Anhui University Industrial Technology Research Institute,Ma’anshan 243000,China
Abstract：Aiming at the problem that only four simple mathematical models in the variable predictive mode based class discriminate（VPMCD）method can not reflect complex relationships among eigenvalues,it is found that the extreme learning machine (ELM) regression model,a complex and widely used one,can reflect relationships among eigenvalues.Here,combining with the advantage of ELM regression model and VPMCD method,a variable predictive mode-based extreme learning machine (VPMELM) method was proposed.It was applied to identify the deterioration state of rolling bearings.The test results showed that the identification method based on VPMELM can effectively to identify the deterioration state of rolling bearings.
郑近德1，潘海洋1,2，童宝宏1，张良安1,2. 基于VPMELM的滚动轴承劣化状态辨识方法[J]. 振动与冲击, 2017, 36(7): 57-61.
ZHENG Jinde1,PAN Haiyang1,2,TONG Baohong1,ZHANG Liang′an1,2. Deterioration state identification method for rolling bearings based on VPMELM. JOURNAL OF VIBRATION AND SHOCK, 2017, 36(7): 57-61.
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