# Diagnosis of the Hollow Ball Screw Preload Classification Using Machine Learning
## 3 Experiment Set-Up and Results
### Servo-Motor Current with the Optical Linear Scale  
For effective electrical fault predictions, the authors of [18] performed multiclass SVM predictions with the current signal only, vibration only, and vibration and current signals concurrently when the induction motor was running with a mechanical load. For light and high mechanical loads, current signals only could be used to perfectly predict the electrical faults. Figure 3 illustrates the plot of the two servo-motor current signals based on the 4% ball screw preload nut without ball-screw pretension. The motor was running at a speed of 600 rpm with a 20-kg payload for 60 min with and without oil-cooling circulation. The feed motor current with oil-cooling circulation was less than that without cooling. An increasing temperature distribution was created along the ball screw and ball nut (Figure 4) when the operation time of the feed-drive system was long [7]. The high temperature between the balls and the ball race of the ball nut amplified the ball size, and therefore, generated additional contact friction area. This resulted in additional torque current being produced without the help of the cooling system. As depicted in Figure 3, the upper and lower sharp peaks of the 4% ball-nut preload motor current occurred when the feed-drive system was under repetitive motion for 60 min, which indicated reverse motion because of the acceleration and deceleration commands. The acquired signals were feedforward, and backward signals that contained information regarding the peak servo current value were associated with the linear scale signals. Figures 5 and 6 display the training and testing signals, respectively, of the sampled data for the SVM analysis based on the 25 forward and backward signals of the maximum peak value. The hyperplane in Figure 5 divided the 2% (red + mark) and 4% (green * mark) ball-nut preload ball screws into two half-spaces when the table was running. Without oil-cooling circulation, the Fisher criterion score was high with low dispersion and high aggregation of the current or positioning feature (Figure 6). The classification rate was 100% because the peak current signal region associated with the positioning linear scale signals was used. The Fisher criterion, dispersion, and aggregation from the motor current, and the position data had high scores whenever the preload was changed or a pretension of 5 \(\upmu\) was applied.  
Figure 3: Motor current plot of the 4% ball-screw preload without pretension and with a 20-kg payload under a repetitive motion speed of 600 rpm for 60 min. The blue and red colors indicate the conditions with and without oil-cooling circulation, respectively.  
Figure 4: Thermal image [16] of the 4% ball-screw preload without pretension and payload under a repetitive motion speed of 900 rpm for 60 min. The temperature distribution of the ball nut for 60 min begins from the top left and ends to the bottom right.  
Figure 5: 100% classification rates obtained for the 2% and 4% ball-nut preloads based on the current and linear scale signals with oil-cooling circulation: (**a**) linear support vector machine (SVM) kernel function, (**b**) radial kernel function, (**c**) linear SVM kernel function with an additional pretension of 5 \(\upmu\), and (**d**) radial kernel function with an additional pretension of 5 \(\upmu\).