Alfredo Garro

Predictive maintenance is increasingly supported by data‐driven methods, which can effectively learn from operational data but may overlook important structural and behavioural knowledge about the monitored system. This paper presents H‐PdM, a hybrid method that combines information derived from system models with machine‐learning‐based analysis of operational data. The two sources provide complementary information that is combined to support fault diagnosis and maintenance decisions. The method is evaluated on a triplex pump digital twin considering different fault mechanisms and their combinations. Several machine learning classifiers are compared, and the final configuration is evaluated under non‐ideal operating conditions, including measurement noise, sensor bias, drift and missing data. The results show that the hybrid approach is more robust than either the data‐driven or model‐driven component used alone. An explainability analysis also shows how system‐derived rules and data‐driven features contribute differently depending on the detected fault. Overall, the results support the use of model and data‐driven information together to achieve more reliable and interpretable predictive maintenance.

Modeling and Simulation (M&S) is a fundamental technology to design and study complex systems in various industrial and scientific domains when real-world testing is impractical due to costs, safety concerns, and time constraints. To promote the reusability and interoperability of simulation entities, allowing them to interoperate without geographic barriers, distributed simulation has been introduced. One of the most widely adopted standards for distributed simulation is IEEE 1516-2010: High Level Architecture (HLA). Among the services provided by HLA, a key one is Data Distribution Management (DDM), which allows to reduce the transmission and reception of unnecessary data to improve communication effectiveness among simulation entities. This is done by computing the so-called “region matching”, i.e., the overlap between “update” and “subscription” regions. In this article, a novel DDM system called the Topic-based publish-subscribe Messaging System (TBMS) is proposed to improve the performance, reliability, and scalability of DDM services. Unlike traditional algorithms, this system revolves around the concept of “topic” that favors a contextualized approach to the match, avoiding the filtering of the information actually of interest on the receiver side. Experiments have been conducted to evaluate the proposed TBMS in terms of utilization and waiting time metrics.

Modeling and Simulation of Cyber-Physical Systems require to deal with new challenges arising from the growing heterogeneity of the involved components and rela Modeling and Simulation of Cyber-Physical Systems require to deal with new challenges arising from the growing heterogeneity of the involved components and rela

This paper considers hybrid systems – dynamical systems that exhibit both continuous and discrete behavior, where interactions between the continuous and discrete dynamics occur when a pre-defined function becomes equal to zero. Thus, de-termination of zero-crossings plays a crucial role in the correct simulation of the system in this case. Correct determination of zero-crossings for a complex real-life system can require a lot of computational resources and, as a consequence, slow down the simulation significantly. This paper presents a new way to execute the simulation generating time observations of the hybrid sys-tem dynamically using numerical infinitesimals introduced by the Infinity Computer, allowing thus to determine zero-crossings correctly. The proposed method allows to automatically detect zero-crossings with any predefined accuracy and to analyze better the behavior of the system around the zero-crossings generating observations more densely, where it is necessary. Moreover, the search for zero-crossings is performed efficiently without re-evaluation of the whole system at any observation. To show the validity of the proposed method, an important real-life hybrid system has been studied and the simulation results have been compared with the standard method.

To capture the dynamics of modern Cyber-Physical Systems, hybrid system models are introduced to combine their continuous dynamics with the discrete ones. Unfortunately, one important negative issue can affect hybrid system models: the so-called Zeno phenomenon, which results in an infinite number of discrete transitions in a finite amount of time occurring during the model’s simulation that leads to inconsistent results. In this context, the paper investigates the use of a recently proposed numerical algorithm, based on the Infinity Computer methodology, to handle the Zeno phenomenon and evaluate it with respect to standard numerical methods by considering the hybrid system models of two exemplary Cyber-Physical Systems: the Water tanks and the Thermostat.