Detection of Machines Anomaly from Log Files in Hard Disk Manufacturing Process

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 amongst those machines and correct them quickly. In an automated production line, all machines are monitored and their log files are sent to a server continuously. Engineers use these log files to diagnose the causes of the errors. This work proposes to use machine learning method to identify the anomaly events in the log files. The experimental results show that it is very accurate and it can help the team of engineers to perform diagnosis quickly and effectively.