Predictive Maintenance of Machine Tools

The study takes an empirical approach by testing the proposed predictive maintenance system in a real industrial scenario. To begin, data is collected from diverse sources within the industrial ecosystem, which includes information gathered through system, which combines data acquired from sensors, devices with PLCs (programmable logic controllers), and different communication protocols. This rich and diverse dataset is then processed and made accessible through a Data Analysis Tool. One of the key strengths of this approach is its reliance on Machine Learning, specifically the logistic regression and support vector machine. It is well-suited for predictive maintenance because of its ability to handle complex datasets and relationships among different variables. The paper details the process of applying the logistic regression and support vector machine approach to the collected data, which allows for the prediction of various machine states. This is crucial in foreseeing potential issues before they escalate into significant failures, enabling timely maintenance interventions. Moreover, the research extends its assessment by comparing the outcomes of the Machine Learning approach to results obtained using simulation tools, thus providing a comprehensive an assessment of the system for predictive maintenance.