Industrial Cyber-Physical System (ICPS) monitoring is increasingly being used to make decisions that impact the operation of the industry. Industrial manufacturing environments such as production lines are dynamic and evolve over time due to new requirements (new customer needs, conformance to standards, maintenance, etc.) or due to the anomalies detected. When an evolution happens (e.g., new devices are introduced), monitoring systems must be aware of it in order to inform the user and to provide updated and reliable information. In this article, CALENDAR is presented, a software module for a monitoring system that addresses ICPS evolutions. The solution is based on a data metamodel that captures the structure of an ICPS in different timestamps. By comparing the data model in two subsequent timestamps, CALENDAR is able to detect and effectively classify the evolution of ICPSs at runtime to finally generate alerts about the detected evolution. In order to evaluate CALENDAR with different ICPS topologies (e.g., different ICPS sizes), a scalability test was performed considering the information captured from the production lines domain.

Monitoring Industrial Cyber-Physical Systems (ICPSs) is increasingly becoming essential, which requires the development of new approaches to capture data from an ICPS for visualization, automated analyses, decision making, and anomaly detection. Towards this end, first, we present requirements for enabling personalized data visualization for monitoring evolving ICPS during its operation. Such requirements were identified from our experience of designing and developing an industrial Automated Warehouse (AW). Second, the identified requirements were validated with a questionnaire-based survey by soliciting views of industry experts from the Software Monitoring Systems and Data Visualizations domains. Based on the analysis of the survey results, the need of developing personalized ICPS monitoring systems was confirmed as a step forward to enable the efficient detection of anomalies, improvement in productivity, and providing a better overview of the monitored ICPSs. Third, we developed a model-based visualization system (named as PAVS) for monitoring ICPSs, which conforms to the identified requirements, and was validated it with a dataset collected from a real AW developed by our industrial partner: ULMA Handling System, Spain. In the end, we also provide a set of lessons learned when PAVS was transferred to ULMA.