Predictive Mantainance: service and data design, development of integrated approaches to machine diagnostics


Today, thanks to the advent of new IoT and ICT technologies introduced with the advent of Industra 4.0, manufacturing industries can easily collect a large amount of information about their products and processes. Such information, thanks to the new technologies of artificial intelligence of big data analytics, can be used profitably both to improve the quality of the products/processes themselves, and to enhance the know-how of companies, and generate new value, by implementing servitization processes.
In this context, the research activity focuses on the study and definition of methods and tools to guide the development and implementation of new predictive maintenance services, according to a CRISP (Cross-Industry Standard Process) approach.


The activities are carried out in the Virtual Prototyping and Virtual Reality laboratory

  1. Colasante, A., Ceccacci, S., Talipu, A., Mengoni, M. “A Fuzzy Knowledge-Based System for Diagnosing Unpredictable Failures in CNC Machine,” submetted to the International Journal of Advanced Manufacturing Technology
  2. Calabrese, M., Cimmino M., Manfrin, M., Fiume, F., Kapetis, D., Mengoni, M., Ceccacci, S., Frontoni, E., Paolanti, M., Carotta, A., Toscano, G. “AN EVENT BASED MACHINE LEARNING FRAMEWORK FOR PREDICTIVE MAINTENANCE IN INDUSTRY 4.0,” submetted to the ASME 2019 International Design Engineering Technical Conferences & Computers and Information in Engineering Conference
Scientific Manager
Working group