Detection of Machines Anomaly from Log Files in Hard Disk Manufacturing
Process using Decision Trees
Thanatarn Pattarakavin, Prabhas Chongstitvatana
Faculty of Engineering
Thai journal of operation research, vol 4, no 2 (July-Dec 2016), pp.10-17.
Hard disk manufacturing is an important industry in Thailand. The production line of its manufacturing process is highly complex and consists of hundreds of automated machines running a continuous flow production. When an anomaly event occurs,the production line has to be stopped and the diagnosis engineering team must identify and locate the source of error with the machines and correct it quickly. In an automated production line, all of the machines are monitored and their log files are sent to a server continuously;engineers then use these log files to diagnose the causes of the errors. This work proposes to use the machine learning method to identify the anomalous events in the log files. such as an anomaly caused by engineers, anomaly from systems, and anomaly from software. The experimental results showed that accuracy was100%using 10-Fold validation, so it is very accurate and it can help teams of engineers perform diagnosis quickly and effectively.
Keywords: hard disk manufacturing, head stack assembly, log files,machine learning,decision tree