A New Recursive Least-Squares Method with Multiple Forgetting Schemes

We propose a recursive least-squares method with multiple forgetting schemes to track time-varying model parameters which change with different rates. Our approach hinges on the reformulation of the classic recursive least-squares with forgetting scheme as a regularized least squares problem. A simulation study shows the effectiveness of the proposed method

Multi-Domain Fault Models Covering the Analog Side of a Smart or Cyber-Physical System

Over the last decade, the industrial world has been involved in a massive revolution guided by the adoption of digital technologies. In this context, complex systems like cyber-physical systems play a fundamental role since they were designed and realized by composing heterogeneous components. The combined simulation of the behavioral models of these components allows to reproduce the nominal behavior of the real system. Similarly, a smart system is a device that integrates heterogeneous components but in a miniaturized form factor. The development of smart or cyber-physical systems, in combination with faulty behaviors modeled for the different physical domains composing the system, enables to support advanced functional safety assessment at the system level. A methodology to create and inject multi-domain fault models in the analog side of these systems has been proposed by exploiting the physical analogy between the electrical and mechanical domains to infer a new mechanical fault taxonomy. Thus, standard electrical fault models are injected into the electrical part, while the derived mechanical fault models are injected directly into the mechanical part. The entire flow has been applied to two case studies: a direct current motor connected with a gear train, and a three-axis accelerometer

Neuro-symbolic Empowered Denoising Diffusion Probabilistic Models for Real-time Anomaly Detection in Industry 4.0

Industry 4.0 involves the integration of digital technologies, such as IoT, Big Data, and AI, into manufacturing and industrial processes to increase efficiency and productivity. As these technologies become more interconnected and interdependent, Industry 4.0 systems become more complex, which brings the difficulty of identifying and stopping anomalies that may cause disturbances in the manufacturing process. This paper aims to propose a diffusion-based model for real-time anomaly prediction in Industry 4.0 processes. Using a neuro-symbolic approach, we integrate industrial ontologies in the model, thereby adding formal knowledge on smart manufacturing. Finally, we propose a simple yet effective way of distilling diffusion models through Random Fourier Features for deployment on an embedded system for direct integration into the manufacturing process. To the best of our knowledge, this approach has never been explored before.Comment: Accepted at the 26th Forum on specification and Design Languages (FDL 2023

Metodo Monte Carlo e generazione di numeri casuali

La generazione di numeri casuali ha affascinato l'uomo sin da tempi antichissimi. Tuttavia solo recentemente il processo è stato velocizzato permettendone l'utilizzo su larga scala anche nella ricerca scientifica. Verranno presentati alcuni dei generatori di numeri casuali in uso o storicamente rilevanti, a fianco delle proprietà che si vorrebbe soddisfacessero, concentrando poi l'attenzione su dei rapidi algoritmi (deterministici) che simulino, sufficientemente bene, variabili aleatorie uniformi, in un senso che sarà chiaro più aventi. Con l'introduzione di un semplice teorema sarà possibile generalizzare i risultati precedenti a qualsiasi funzione di densità (o distribuzione di probabilità). Seguirà l’esposizione del funzionamento di alcuni test in grado di mettere in evidenza il non soddisfacimento di alcune delle proprietà fondamentali sopra citate, concludendo con un'applicazione pratica di grande rilevanza per il mondo scientifico contemporaneo: il metodo Monte Carl

Controllo distribuito ed in retroazione della potenza reattiva per la regolazione di tensione e la minimizzazione delle perdite. Distributed reactive power feedback control for voltage regulation and loss minimization

Sfruttando i microgeneratori dislocati in una smart grid si forniscono iniezioni di potenza reattiva con l'obiettivo di controllare le tensioni nodali entro un intervallo di tolleranza. Si esaminano alcuni algoritmi presenti in letteratura e viene proposta una strategia di controllo finalizzata in primo luogo alla voltage regulation ed in secondo luogo alla minimizzazione delle perdite di potenza. Le prestazioni degli algoritmi descritti sono analizzate attraverso simulazioni in matla

Inferring Mechanical Fault Models from the Electrical Domain

In the context of Industry 4.0, it is strategic to build a simulable model of an Industrial Cyber-Physical System (CPS) to ensure proper maintenance and early risk assessment to avoid monetary losses. To achieve this, it is necessary to use dedicated techniques for modeling and injecting faults into a simulative model. However, it is generally too complex due to heterogeneous components, e.g., analog and digital parts. Verilog-AMS is a suitable solution to overcome this problem since it allows the covering of different physical descriptions, starting from transistor-level to multi-discipline models (e.g., mechanic, thermic, fluid dynamic). This article proposes a methodology that exploits the specific analogy between mechanical and electrical domains. It starts from a mechanical model, builds the electrical equivalent, and injects electrical faults. The analysis of the injected faults allows building a generic taxonomy for mapping electrical faults onto mechanical ones. The final goal is to support the construction of Failure Mode and Effect Analysis (FMEA) principles in mechanical systems and the prospect of enabling predictive maintenance techniques

The Challenges of Coupling Digital-Twins with Multiple Classes of Faults

In modern industrial contexts, a factory becomes a complex and heterogeneous ecosystem, where many technologies, systems, and workers cooperate. Such a class of systems is named Cyber-Physical Production Systems (CPPSs), since their design requires to merge control, network, and physical aspects. In such a context, it is fundamental to guarantee safe human-machine interactions. Therefore, evaluating and adopting techniques is necessary to ensure functional safety. This article analyzes the challenges of creating digital twins coupled with multiple classes of faults to simulate and verify the system under design. In particular, challenges can be collected under three main categories: modeling, simulation and assessment. Exploiting a language capable of capturing the complexity of such systems is necessary to model CPPSs and support the creation of digital twins. Efficient simulation of CPPSs needs different abstraction techniques and requires to combine discrete and continuous components. Moreover, different classes of faults must be injected into the models to verify the cyber and the physical parts. This would allow assessing the functional safety of each machinery composing the plant

A Framework for Modeling and Concurrently Simulating Mechanical and Electrical Faults in Verilog-AMS

There are several languages for modeling a Cyber-Physical System (CPS). One of them is Verilog-AMS, which allows representing a system belonging to the electrical and mechanical physical domains in a single model through different disciplines. A framework for the automatic fault injection in the electrical and mechanical domains is proposed in this context. In particular, starting from a mechanical system, it is possible to represent it as an electrical circuit by exploiting the physical analogies. In the electrical domain, fault modeling and injection techniques are more advanced than in other physical domains. Extending the analogies to fault models makes it possible to apply the electrical fault models in the equivalent circuit to the mechanical system. These yields mechanical-level faulty behaviors, which can be injected into the mechanical domain, resulting in mechanical (physical) faults, depending on the component. It is finally shown an example of execution of this flow through a model of an electric motor, in which mechanical faults are injected. Simultaneously, the equivalent electrical faults are injected into the equivalent electrical circuit

A SystemC-based Simulator for Design Space Exploration of Smart Wireless Systems

Smart wireless techniques are at the core of many today's telecommunication and networked embedded systems where performance are enhanced by intertwining radio frequency (RF) and digital aspects. Therefore their design requires to focus on both domains. Traditional approaches for their simulation rely either on different domain-specific tools or on analog-mixed-signal modeling languages. In the former case, the simulation of the whole platform in the same run is not possible while in the latter case, simulation performance are limited by the computationally most intensive domain (usually RF). We present an extension of the SystemC Network Simulation Library that allows to simulate antenna details and node position together with digital hardware and software. The validation on a real wearable system shows that the proposed simulation approach achieves a good trade-off between accuracy and speed thus allowing fast exploration of various configurations in the early phase of the design flow without recurring to the expensive and time-consuming creation of physical prototypes

Thermal Digital Twin of a Multi-Domain System for Discovering Mechanical Faulty Behaviors

Constructing a holistic digital twin of a system composed of multiple physical domains is crucial for various tasks. In particular, when the simulation is extended with faults, it becomes a very important resource to achieve robust functional safety analysis. This article proposes a new methodology to build non-electrical fault models for the thermal domain. Such thermal faults are defined through an electrical circuit representing the thermal behavior of the system, known as the Cauer network, based on the physical analogies between the two domains. Including this thermal representation in a multi-domain system allows to simulate the interconnections between different physical domains, thus achieving a more realistic system behavior and evaluating the mutual impact of different domains (e.g., mechanical, electrical and thermal). The entire methodology is applied to a complex case of study implemented by using Verilog-AMS as a proof of concept