Detection of Machines Anomaly from Log Files in Hard Disk Manufacturing
Process using Decision Trees
Thanatarn Pattarakavin, Prabhas Chongstitvatana
Faculty of Engineering
Chulalongkorn University
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