Text mining techniques for the management of predictive maintenance

The advent of Industry 4.0 provides new opportunities to improve the maintenance of production equipment from both the technical and managerial perspective. In this paper, we propose a contribution in the direction of predictive maintenance of machine tools based on the integration of a text mining algorithm with the cyber-physical system of a manufacturing industry. The system performs its analysis starting from data stored in log files maintained by a machine tool returning an alert about a future potential machine failure. Log files, produced by part programs running on the machine control system, record the status of execution parameters taken by key sensors or derived by the control system during the part program execution. Historical data are collected by means of Digital Twin technologies and then analyzed using computational linguistic techniques so that we can predict a machine failure in the imminent future starting from data collected in the past. The paper first describes a new scheme for the classification of maintenance approaches. Then, starting from the proposed cyber-physical system model, an algorithm for predictive maintenance based on text mining technology is integrated in it. The implemented tool supports the maintenance manager in making the most appropriate decisions about the scheduling of maintenance activities when there is an alert about a possible machine failure.