Hollow Ball Screw Nut Preload Diagnosis by Support Vector Machine with K-Fold Cross Validation

• Main Authors: Chang,Chih-Hsiang, 張智翔
• Other Authors: Huang, Yi-Cheng
• Format: Others
• Language: zh-TW
• Published: 2018
• Online Access: http://ndltd.ncl.edu.tw/handle/k99q42

碩士 === 國立彰化師範大學 === 機電工程學系 === 106 === The purpose of this thesis is conducting a diagnosis method for the ball screw nut with different preload by analyzing signals from different operation conditions. This research focus on how to diagnosing the feed drive status of the machine tool based on short warm-up time before manufacturing. Since it cost long time operation for industrial ball screw turning into failure mode. This research changes different ball nut preload by 2%, 4 % and 6 % of the maximum dynamic in experiments. Motor load current, linear encoder signal and motor revolution speed signal were acquired and adopted for Support Vector Machine (SVM). Linear kernel function and radial basis function kernel function were used as for classification hyperplane. For bettering parameters of SVM classification, the k-fold cross validation is used. Experimental results show that it is possible to distinguish different ball nut preload status via deploying motor current, linear scale and motor revolution speed signals into SVM with k-fold classification. Experimental results show the early warning module for ball screw failure is successful and promising by developing SVM with k-fold cross validation method.