关键词: 概率输出, 弹性凸包, 滚动轴承, 故障诊断

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.

Key words: probability output, flexible convex hull, rolling bearing, fault diagnosis