Experiential learning of Prognostics and Health Management and its implementation in MATLAB

Nowadays, smart factories more and more rely on key enabling technologies to optimize the management of operations. In the maintenance context, predictability is a major characteristic required for advanced monitoring and controlling systems, embedded in Cyber-Physical Systems (CPS), which are the building blocks of smart factories. As such, methodologies and tools proper of the Prognostics and Health Management (PHM) body of knowledge, represent the background on which a company should build their competitive advantage. However, promoting the application of PHM in current industrial scenario is not only a matter of digital technologies, but it encompasses engineering methodologies. These methodologies should be made available to learners so to transfer knowledge to industry. Therefore, a learning-by-doing approach is proposed, which aims at showing how the current software tools provide per se a complete platform for PHM for teaching purposes, without a strong requirement of real testbeds, at least at first sight. Also, the selection of MATLAB allows to transfer knowledge to learners with few or no programming skills. It is demonstrated how the engineering methodologies and tools underlying a robust PHM system could be developed during lectures independently from the availability of a laboratory or industry-like environment if the key characteristics of PHM are properly formalised. Therefore, the basic idea is to support the dissemination of a practical background about PHM, both in physical and virtual classrooms, aimed at providing advanced understanding of CPS-based smart factories.