Abstract:Aiming at the defects that the classification hyperplane of flexible convex hull were only formed by constraints of convex hull vertex and ignores decision-making contributions of most of the other sampling points, which led to the reduction of classification accuracy. A probability output flexible convex hull classification method was proposed. In this method, distance from all the samples to the hyperplane was calculated after the convex vertices were used to form the hyperplane. Through sparse mapping, cross entropy likelihood function was minimized and transformed into a posteriori probability output. When dealing with multi classification problems, output probability of each classifier was coupled in pairs, which further improved classification accuracy and robustness of the multi classification flexible convex hull. This method was applied to the fault diagnosis of rolling bearings, and the robustness was verified on different data sets, which may accurately identify the faults of rolling bearings.
杨路航1;李宝庆1;王平2,3;王健2,3;杨宇1. 基于概率输出弹性凸包的滚动轴承故障诊断方法[J]. 中国机械工程, 2021, 32(01): 40-46.
YANG Luhang1;LI Baoqing1;WANG Ping2,3;WANG Jian2,3;YANG Yu1. Fault Diagnosis Method of Rolling Bearings Based on Probability Output Flexible Convex Hull. China Mechanical Engineering, 2021, 32(01): 40-46.
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