A meta-learning approach to select appropriate prognostic methods for the predictive maintenance of digital manufacturing systems

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Maintenance planning is of paramount importance for manufacturing companies in order to assure a constant availability of their machines and to avoid high repair costs. The technological developments of the last years, which can be summarized under the terms Industry 4.0 and Advanced Digitalization, offer new potentials to describe the state of a digitalized manufacturing system in real-time. This provides a basis for moving from classical reactive or periodic maintenance planning to more efficient condition-based and predictive maintenance. However, the suitability and performance of prognostic methods to predict the future failure behavior of machines and components depends strongly on the state of a machine, its components and their configuration. Hence, the suitability can change over time. Despite the large amount of research regarding predictive maintenance in the last years, there is a clear research gap regarding a method to select appropriate prognostic methods for predictive maintenance depending on the current state of a manufacturing system and its machines.The objective of this joint research project is to develop a meta-learning system that selects suitable prognostic methods for predictive maintenance depending on the current state of a manufacturing system using sensor data of the machines. The project will be jointly conducted by three research groups at (i) BIBA - Bremer Institut für Produktion und Logistik at the University of Bremen, Germany, (ii) the Federal University of Santa Catarina (UFSC), Florianopolis, Brazil, and (iii) the Federal University of Rio Grande do Sul (UFRGS), Porto Alegre, Brazil, who have complementary research profiles. The research group at BIBA will develop a meta-learning method to select suitable prognostic methods based on historical and sensor data. The research group at UFSC will develop an integrated production and maintenance planning method. The research group at UFRGS will develop a service-oriented architecture to assure an appropriate data-exchange between a manufacturing system and the prognostic as well as the planning method. Integrating these different modules, the result of the proposed project will be a meta-learning predictive maintenance system that will be capable to (i) use sensor data of digitalized manufacturing systems to describe the system state in real-time, (ii) select suitable prognostic methods dynamically based on the state of a machine and it’s components, (iii) compute an integrated production and maintenance plan and (iv) evaluate the performance of the selected prognostic methods as well as the subsequent planning decisions by their forecast errors and, in addition, by using logistic key performance indicators such as machine utilization and throughput times. In this way, the meta-learning predictive maintenance system will help a manufacturing company to achieve a better production and maintenance planning and an increased production performance.