Study on Detection of Bearing Groove Wear of Milling Machine under Improved K-Mean Clustering
Aiming at the problem of low accuracy of bearing groove wear detection of milling machine,an detection method of bearing groove wear of milling machine under improved K-mean clustering is proposed.Through the UT372 handheld photoelectric velocimeter and MPU-605 piezoelectric accelerometer,the milling machine bearing groove wear data are collected.The clustering center of the dataset is initially selected based on the farthest-nearest principle.The distance among the points in the data set and the average distance of all the data are calculated using the Euclidean distance,and the two thresholds of the clustering center are determined by combining with cross-validation.Canopy algorithm is introduced to improve the K-mean clustering,to determine the global best clustering center and realize the intelligent detection of the bearings groove wear of milling machine.The experimental results show that the improved K-mean clustering algorithm in the detection of milling machine bearing groove wear,the number of iterations is fixed to 15 times,Jaccard coefficient are extremely close to 1.This method can significantly improve the computational speed and stability of the clustering and can identify different types of milling machine bearing groove wear faults,and the detection accuracy is high.
万方数据
维普
