Making Factories Smarter Through Machine Learning
Figure 4 : Reducing the Big Data to Critical Metadata to Get to the Smart Data
One example of an application is when the tool tip is monitored for process deviations . Note , there are many types of process deviations possible depending on the nature of the product line operation and the required cycle time – there are many types of tools : A typical CNC machine has multiple tools with many different types of tool tips ( laser , milling , drilling , grinding , tapping , etc .).
Temperature fluctuations cause most common deviations . In these cases , variables like position and temperature in different points has to be measured . If 14 measuring points are selected ( mainly different temperatures and tool tip position ), using a sample rate of 5 minutes can give enough data to produce 11.2 KB / day ( 4 MB / year ). This data with a supervised learning algorithm can increase the machine availability by 20 %, which is nearly 3 hours / day .
Another example is the operation behavior of servomotors . In this case , variables like torque , power , temperature , vibration and angular speed can be measured . The number of variables could be up to 15,000 . However , using feature subset selection , the reduction could leave only 50 variables and 1 TB / year . Figure 5 shows the results of using Unsupervised Learning to identify patterns relative to Power Consumption , Torque and other variables generating a “ fingerprint ” of the servo drive , where servomotor behavior is represented in the X-axis .
- 36 - January 2017