The progressive and pervasive digitization of industry brings a paramount opportunity to enhance the productivity of manufacturing and assembly operations. In this context, state-of-the-art production systems implement Predictive Maintenance (PdM) solutions to complement preventive maintenance scheduling and avoid costly unexpected breakdowns and corrective maintenance actions, and this where the SERENA R&D project is framed.
SERENA was built upon the needs for saving time and money, minimizing the costly production downtimes, covering the requirements for versatility, transferability, remote monitoring and control by a) a plug-and-play cloud-based communication platform for managing the data and data processing remotely, b) advanced IoT system and smart devices for data collection and monitoring of machinery conditions, c) artificial intelligence methods for predictive maintenance and planning of maintenance and production activities, d) AR-based technologies for supporting the human operator for maintenance activities and monitoring of the production machinery status.
In this sense, the main difference of SERENA with existing solutions is based on the operator support service; integrated along with the entire predictive maintenance solution and real-time data monitoring. This interface helps technicians to identify the source of the error and go through determined steps in the problem-solving procedure, including all visualization systems and AR tools, as well as the scheduling options integrated, or working along, with this system.
In addition, SERENA was built to be flexible and scalable merging cloud based and edge deployed components. Through the SERENA architecture and its outcomes, various applications can be enabled with cyber-physical features within the Industrie4.0 and connected factories vision. In this regard, the SERENA system addresses some common needs of industrial enterprises:
# compatibility with both the on-premise and the in-the- cloud environments
# exploitation of reliable and largely supported Big Data platforms
# virtually unlimited horizontal scalability
# easy deployment through containerized software modules
