Real-world failure prevention framework for manufacturing facilities using text data

In recent years, manufacturing companies have been continuously engaging in research for the full implementation of smart factories, with many studies on methods to prevent facility failures that directly affect the productivity of the manufacturing sites. However, most studies have only analyzed sensor signals rather than text manually typed by operators. In addition, existing studies have not proposed an actual application system considering the manufacturing site environment but only presented a model that predicts the status or failure of the facility. Therefore, in this paper, we propose a real-world failure prevention framework that alerts the operator by providing a list of possible failure categories based on a failure pattern database before the operator starts work. The failure pattern database is constructed by analyzing and categorizing manually entered text to provide more detailed information. The performance of the proposed framework was evaluated utilizing actual manufacturing data based on scenarios that can occur in a real-world manufacturing site. The performance evaluation experiments demonstrated that the proposed framework could prevent facility failures and enhance the productivity and efficiency of the shop floor.