地铁车辆车轮轮缘异常磨耗检测中的箱线图算法和改进孤立森林算法

Boxplot Algorithm and Improved Isolated Forest Algorithm in Metro Vehicle Wheel Flange Abnormal Wear Detection

对某地铁列车48个车轮的实测廓形监控数据进行了批量分析,以轮缘磨耗面积变化速率和轮缘根部滚动圆半径差之半变化速率为分析指标,分别采用箱线图算法和改进孤立森林算法进行磨耗异常检测。箱线图算法具备较好的抗干扰能力,可得到对各指标单独检测的客观统计结果;改进孤立森林算法运行效率得到提高。经对比,2种算法得到的磨耗异常检测结论较一致,验证了两种算法的可行性。

The measured profile monitoring data of 48 wheels of a metro train is analyzed in batches.Taking the change rate of the flange wear area and the half change rate of the rolling circle radius difference at the flange root as analysis indexes,the boxplot algorithm and the improved isolated forest algorithm are used to detect the wear anomaly respectively.The boxplot algorithm has good anti-interference ability and can obtain objective statistical results of independent detection of each index.While the operation efficiency of the improved isolated forest algorithm is elevated.By comparison,the wear anomaly detection results obtained by the two algorithms are consistent,verifying the feasibility of both.