publications | Nicola Dall'Ora

2026

Sustainable SoftPLCs at the Edge: An Open-Source Path to Right-Sizing and Retrofitting Industrial Automation

Gioacchino Ivan Noto , and Nicola Dall’Ora

2026 IFIP/IEEE International Conference on Very Large Scale Integration (VLSI-SoC), 2026
Special Session: Quantifying the Effect of HCLs on a Fixed-Microarchitecture MXFP4 Accelerator

Daniele Passaretti , Sajjad Tamimi , and Nicola Dall’Ora

2026 Forum on Specification & Design Languages (FDL), 2026

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MATE: A Methodology for Connecting Automatic Test Equipment in Industry 4.0

Francesco Biondani , Francesco Tosoni , Nicola Dall’Ora , Enrico Fraccaroli , Dong Seon Cheng , and 1 more author

IEEE Access, 2026

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A Conceptual Framework for Athlete Health Using AIoT, Wearables, and Personalized Performance Intelligence

Ernesto William De Luca , Nicola Dall’Ora , Romeo Giuliano , Carlo Valle , Alessandra Cagno , and 9 more authors

Applied Sciences, 2026

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A Hybrid LoRa/ZigBee IoT Mesh Architecture for Real-Time Performance Monitoring in Orienteering Sport Competitions: A Measurement Campaign on Different Environments

Romeo Giuliano , Stefano Alessandro Ignazio Mocci De Martis , Antonello Tomeo , Francesco Terlizzi , Marco Gerardi , and 3 more authors

Future Internet, 2026

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Process mining-driven modeling and simulation to enhance fault diagnosis in cyber-physical systems

Francesco Vitale , Nicola Dall’Ora , Sebastiano Gaiardelli , Enrico Fraccaroli , Nicola Mazzocca , and 1 more author

Journal of Manufacturing Systems, 2026

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2025

LumiCare: A Context-Aware Mobile System for Alzheimer’s Patients Integrating AI Agents and 6G

Nicola Dall’Ora , Lorenzo Felli , Stefano Aldegheri , Nicola Vicino , and Romeo Giuliano

Electronics, 2025

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Human-Centered Digital Twin for Industry 5.0

Francesco Biondani , Luigi Capogrosso , Nicola Dall’Ora , Enrico Fraccaroli , Marco Cristani , and 1 more author

2025 Design, Automation & Test in Europe Conference (DATE), 2025

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Moving beyond the automation-driven paradigm of Industry 4.0, Industry 5.0 emphasizes human-centric industrial systems where human creativity and instincts complement precise and advanced machines. With this new paradigm, there is a growing need for resource-efficient and user-preferred manufacturing solutions that integrate humans into industrial processes. Unfortunately, methodologies for incorporating human elements into industrial processes remain underdeveloped. In this work, we present the first pipeline for the creation of a human-centered Digital Twin (DT), leveraging Unreal Engine’s MetaHuman technology to track worker alertness in real-time. Our findings demonstrate the potential of integrating Artificial Intelligence (AI) and human-centered design within Industry 5.0 to enhance both worker safety and industrial efficiency.
Special Session: D-MATE: A Design Methodology for Connecting Automatic Test Equipment in Industry 4.0

Francesco Biondani , Francesco Tosoni , Nicola Dall’Ora , Enrico Fraccaroli , Sara Vinco , and 2 more authors

2025 IEEE 26th Latin American Test Symposium (LATS), 2025

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2024

Digital Twin Integration using Lingua Franca and FMI for Testing Factory Automation Software

Pietro Turco , Elisa Zanella , Andrea Valentini , Sebastiano Gaiardelli , Nicola Dall’Ora , and 2 more authors

IECON 2024-50th Annual Conference of the IEEE Industrial Electronics Society, 2024

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The continuous evolution of Industry 4.0 demands advanced solutions for optimizing production processes, reducing energy consumption, and enhancing the capabilities of both human operators and production devices in smart factories. As factory automation software becomes increasingly complex, ensuring its robustness through extensive testing is critical. However, testing on actual production systems is often impractical due to the critical nature of the software.This paper explores the use of digital twins to simulate factory environments, enabling thorough validation of automation software. We propose leveraging Lingua Franca and the Functional Mock-up Interface (FMI) standard to create comprehensive digital twins. Lingua Franca ensures deterministic execution in simulations, while FMI facilitates the integration of diverse simulators for accurate modeling of heterogeneous systems. We introduce an interfacing method to integrate FMI components within Lingua Franca and present a strategy for modeling software-machinery interactions using the OPC Unified Architecture (OPC UA) protocol. The methodology is applied to develop a digital twin of a logistics subsystem in a manufacturing system, demonstrating the effectiveness of the simulation environment through various scenarios.
Fault Injection for Synthetic Data Generation in Aircraft: A Simulation-Based Approach

Francesco Biondani , Nicola Dall’Ora , Francesco Tosoni , Enrico Fraccaroli , Domenico Fabio Migliore , and 2 more authors

2024 IEEE 22nd International Conference on Industrial Informatics (INDIN), 2024

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The safety of aircraft heavily depends on the integrity of the Landing Gear System (LGS). However, gathering real-world fault data to support effective Prognostic and Health Management (PHM) practices presents significant challenges. This work proposes a novel methodology for generating synthetic fault data using a multi-physics Simscape model of a landing gear deployment/retraction mechanism. The model incorporates specialized fault blocks designed to replicate various hydraulic failure modes, aiming to broaden the pool of fault data covering the most common failures. This approach promises to enhance maintenance strategies and facilitate the development of hybrid Model-Based and Data-Driven solutions. Ultimately, the results of this study will be used to understand the physics within the landing gear better and gather the necessary data to create an effective Digital Twin for predictive maintenance.
Cross-domain Analog Fault Injection for Designing Robust Smart Systems

Francesco Tosoni , Nicola Dall’Ora , Enrico Fraccaroli , Sara Vinco , and Franco Fummi

2024 Forum on Specification & Design Languages (FDL), 2024

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Under the pressure of the Industry 4.0 revolution, and now with the European Chips Act, smart systems are becoming omnipresent in all industrial sectors, e.g., automotive and aerospace. Such systems contain digital and analog components belonging to several physical domains, e.g., electrical and mechanical. To ensure robustness, the whole system must be validated as early as possible in the development cycle, by taking into account all such domains, as recommended by the ISO 26262 standard in the case, e.g., of automotive systems. Unfortunately, validation techniques, including fault injection and simulation are not as advanced on the analog side as the digital counterpart: i) they are not fully standardized ii) they are highly domain-dependent, and iii) they are performed separately from the digital flow. This article proposes to improve the design of smart systems by generating faulty scenarios through analog fault injection across several physical domains. By exploiting these faulty scenarios, it is possible to improve the robustness of the analog part and, at the same time, to improve the quality of the digital part that controls the system functionality. A multi-domain case study containing a microcontroller and a three-axis accelerometer is presented to demonstrate the validity of the proposed approach in many industrial contexts.
Exploring Multidomain Faults in Digital Twin: A Gaming Engine Perspective : Wild-and-Crazy-Idea Paper

Francesco Tosoni , Muhammad Ihtisham Amin , Nicola Dall’Ora , Enrico Fraccaroli , and Franco Fummi

2024 Forum on Specification & Design Languages (FDL), 2024

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Creating a virtual model of a real system brings several advantages before its effective fabrication, especially in the design phases. Simulating a system is helpful for determining whether and what improvements need to be brought to the prototype or for testing changes given by the presence of faults. In this context exploiting the features of a gaming engine created mainly for video game purposes for simulating physical prototypes could reveal new research perspectives. Advanced rendering capabilities, physical simulation, and accuracy in describing different materials obtainable make this environment very attractive even beyond the gaming world. Another key feature that can be obtained with these simulators is the combination of accurately modeled physical properties and graphical rendering of physical behaviors. Together, they generate a visualization that is physically accurate and graphically realistic. For this reason, this article examines the development of behavioral models inside the Unreal Engine gaming environment, mainly based on the C++ language. The models created will be used for design analysis, performance monitoring, and improvement research. The model chosen as a case study is a digital twin of a DC Motor simulated in normal operating conditions and in the presence of faults.
Assessing Robustness of Smart Systems via Multi-domain Analog Fault Simulation

Francesco Tosoni , Nicola Dall’Ora , Enrico Fraccaroli , Sara Vinco , and Franco Fummi

2024 IEEE 30th International Symposium on On-Line Testing and Robust System Design (IOLTS), 2024

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Smart systems contain digital and analog components of several physical domains, e.g., electrical and mechanical During the design phase, the fault injection, which checks the system functionality following the guidelines of ISO standard 26262, enhances the system’s robustness. Unfortunately, fault injection and simulation on the analog side are i) not fully standardized compared to their digital counterparts, ii) highly domain-dependent, and iii) performed separately from the digital. This article proposes to improve the design of smart systems by generating faulty scenarios through analog fault injection across several physical domains. By exploiting these faulty scenarios it is possible to improve the robustness of the analog part and simultaneously improve the quality of the digital part that controls the system functionality.
Analog Fault Simulation: Trends and Perspectives in Analog Hardware Description Languages

Nicola Dall’Ora , Enrico Fraccaroli , Renaud Gillon , and Franco Fummi

2024 IEEE Latin American Test Symposium (LATS), 2024

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Analog Hardware Description Languages (AHDLs) provide a valuable alternative to existing proprietary means of implementing defect models and generic templates. Analog defect modeling in SPICE engines and in event-driven digital simulators is discussed, with a review of the state-of-the-art, an analysis of possibilities, and proposals for future enhancements of tools and standards to meet the challenges of achieving good coverage estimations at the system level. Moreover, we discuss the possibilities of using the EDACurry open-source framework to instrument transistor-level analog circuits to support defects templates written through AHDL, e.g., Verilog-A.
A Data Fusion Service-Oriented Infrastructure for Production Line Monitoring

Sebastiano Gaiardelli , Nicola Dall’Ora , Francesco Ponzio , Enrico Fraccaroli , Franco Fummi , and 2 more authors

2024 IEEE International Conference on Industrial Technology (ICIT), 2024

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The Industry 4.0 paradigm has deeply changed classical manufacturing by introducing data-based analytics and decision-support strategies. At the state of the art, data used for manufacturing monitoring is mostly originated by sensors, that undergo a fusion step to align different data sources. However, this data is only relative to the monitored process, and it does not include the corresponding operating conditions and parameters, that are known by the Manufacturing Execution System (MES). Such information is currently either not included or labeled by hand, thus incurring in errors and limiting the amount of available labeled data. To overcome this issue and go beyond the sole data fusion of sensor data, this paper proposes an infrastructure that automatically label time series generated by sensors with information extracted from the MES, to achieve enhanced monitoring of the production process. The relevance of the proposed solution and the possibilities opened by its application are stressed with the application to a robotic arm.
VARADE: a Variational-based AutoRegressive model for Anomaly Detection on the Edge

Alessio Mascolini , Sebastiano Gaiardelli , Francesco Ponzio , Nicola Dall’Ora , Enrico Macii , and 3 more authors

2024 61th ACM/IEEE Design Automation Conference (DAC), 2024

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Detecting complex anomalies on massive amounts of data is a crucial task in Industry 4.0, best addressed by deep learning. However, available solutions are computationally demanding, requiring cloud architectures prone to latency and bandwidth issues. This work presents VARADE, a novel solution implementing a light autore- gressive framework based on variational inference, which is best suited for real-time execution on the edge. The proposed approach was validated on a robotic arm, part of a pilot production line, and compared with several state-of-the-art algorithms, obtaining the best trade-off between anomaly detection accuracy, power consumption and inference frequency on two different edge platforms.
An AI-Enabled Framework for Smart Semiconductor Manufacturing

Khaled Sidahmed Sidahmed Alamin , Davide Appello , Alessandro Beghi , Nicola Dall’Ora , Fabio Depaoli , and 10 more authors

2024 Design, Automation & Test in Europe Conference & Exhibition (DATE), 2024

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With the rise of Machine Learning (ML) and Artificial Intelligence (AI), the semiconductor industry is undergoing a revolution in how it approaches manufacturing. The SMART-IC project (DATE’24 MPP category: initial stage) works in this direction, by proposing an AI-enabled framework to support the smart monitoring and optimization of the semiconductor manufacturing process. An AI-powered engine examines sensor data recording physical parameters during production (like gas flow, temperature, voltage, etc.) as well as test data, with different goals: (1) the identification of anomalies in the production chain, either offline from collected data-traces or online from a continuous stream of sensed data; (2) the forecasting of new data of the future production; and (3) the automatic generation of synthetic traces, to strengthen the data-based algorithms. All such tasks provide valuable information to an advanced Manufacturing Execution System (MES), which reacts by optimizing the production process and management of the equipment maintenance policies. SMART-IC is a 300kC academic project funded by the Italian Ministry of University and supported by STMicroelectronics and Technoprobe with industrial expertise and real-world applications. This paper shares the view of SMART-IC on the future of semiconductor manufacturing, the preliminary efforts, and the future results that will be reached by the end of the project, in 2025.
Multidomain Fault Models Covering the Analog Side of a Smart or Cyber–Physical System

Francesco Tosoni , Nicola Dall’Ora , Enrico Fraccaroli , Sara Vinco , and Franco Fummi

IEEE Transactions on Computers, 2024

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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.

2023

Robotic Arm Dataset (RoAD): A Dataset to Support the Design and Test of Machine Learning-Driven Anomaly Detection in a Production Line

Alessio Mascolini , Sebastiano Gaiardelli , Francesco Ponzio , Nicola Dall’Ora , Enrico Macii , and 3 more authors

IECON 2023-49th Annual Conference of the IEEE Industrial Electronics Society, 2023

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The early detection of anomalous behaviors from a production line is a fundamental aspect of Industry 4.0, facilitated by the collection of massive amounts of data enabled by the Industrial Internet of Things. Nonetheless, the design and validation of anomaly detection algorithms, mostly based on sophisticated Machine Learning models, heavily rely on the availability of annotated datasets of realistic anomalies, which is very difficult to obtain in a real production line. To address this problem, we introduce the Robotic Arm Dataset (RoAD), specifically designed to support the development and validation of Multivariate Time Series Anomaly Detection (MTSAD) algorithms. We collect and annotate a large number of data and metadata to characterize the motion and energy consumption of a collaborative robotic arm in a full-fledged production line and annotate a comprehensive set of healthy as well as realistic anomalies scenarios. To prove the significance of RoAD and encourage future developments, we benchmark several state-of-the-art anomaly detection algorithms on our newly introduced dataset, and we freely release it to the scientific community.
VIR2EM: VIrtualization and Remotization for Resilient and Efficient Manufacturing: Project-Dissemination Paper

Alessandro Beghi , Nicola Dall’Ora , Davide Dalle Pezze , Franco Fummi , Chiara Masiero , and 3 more authors

2023 Forum on Specification & Design Languages (FDL), 2023

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In this paper, we present the project “VIR2EM: VIrtualization and Remotization for Resilient and Efficient Manufacturing” by providing details on its research themes and its scientific and technological output. The project, centered on virtualization and remotization in the industrial sector, was promoted by Regione Veneto in Italy, and it has seen the participation and collaboration of 3 universities, 1 public research entity, and 10 companies composed of end users of digital solutions and high knowledge-intensive service providers. The project aims to develop and use tools for the virtualization of processes, systems, resources, and remoting of operations in order to: (1) maximize the efficiency of manufacturing systems under normal operating conditions; (2) maintain operations in case of emergency situations; (3) facilitate the restart of operations downstream of emergency situations by ensuring flexibility and predictive capability. Each theoretical proposal has been validated in distinct industrial facilities by constructing ten different prototypes.
Neuro-Symbolic Empowered Denoising Diffusion Probabilistic Models for Real-Time Anomaly Detection in Industry 4.0: Wild-and-Crazy-Idea Paper

Luigi Capogrosso , Alessio Mascolini , Federico Girella , Geri Skenderi , Sebastiano Gaiardelli , and 6 more authors

2023 Forum on Specification & Design Languages (FDL), 2023

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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.
Analog Defect Injection and Fault Simulation Techniques: A Systematic Literature Review

Sadia Azam , Nicola Dall’Ora , Enrico Fraccaroli , Renaud Gillon , and Franco Fummi

IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems, 2023

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Since the last century, the exponential growth of the semiconductor industry has led to the creation of tiny and complex integrated circuits, e.g., sensors, actuators, and smart power. Innovative techniques are needed to ensure the correct functionality of analog devices that are ubiquitous in every smart system. The ISO 26262 standard for functional safety in the automotive context specifies that fault injection is necessary to validate all electronic devices. For decades, standardization of defect modeling and injection mainly focused on digital circuits and, in a minor part, on analog ones. An initial attempt is being made with the IEEE P2427 draft standard that started to give a structured and formal organization to the analog testing field. Various methods have been proposed in the literature to speed up the fault simulation of the defect universe for an analog circuit. A more limited number of papers seek to reduce the overall simulation time by reducing the number of defects to be simulated. This literature survey describes the state-of-the-art of analog defect injection and the fault simulation methods. The survey is based on the preferred reporting items for systematic reviews and meta-analyses (PRISMA) methodological flow, allowing for a systematic and complete literature survey. Each selected paper has been categorized and presented to provide an overview of all the available approaches. In addition, the limitations of the various approaches are discussed by showing possible future directions.
Thermal digital twin of a multi-domain system for discovering mechanical faulty behaviors

Francesco Tosoni , Nicola Dall’Ora , Enrico Fraccaroli , Sara Vinco , and Franco Fummi

2023 IEEE 21st International Conference on Industrial Informatics (INDIN), 2023

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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.
Verilog-A Implementation of Generic Defect Templates for Analog Fault Injection

Nicola Dall’Ora , Sadia Azam , Enrico Fraccaroli , Renaud Gillon , and Franco Fummi

Proceedings of the Great Lakes Symposium on VLSI 2023, 2023

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With functional safety being increasingly important in the development of mixed-signal products for automotive applications, EDA solutions have appeared striving to help designers in the setup and execution of fault injection campaigns. Despite the ongoing work to standardize the definition of defect models and coverage calculation methods in the IEEE P2427 draft standard, there is a lack of a unified and portable method to define defect templates that can be used to inject in a systematic way defects in an analog circuit. Each of the existing EDA tool sets for fault injection proposes its own proprietary method to specify how defects should be defined and injected. The proposed paper describes a Verilog-A-based approach to coding defect templates, which through compliance with the Verilog-A standard, warrants portability across compatible simulators. The approach has been validated on the circuits from the Analogue Benchmark Circuits made available by the IEEE P2427 working group.

2022

A framework for modeling and concurrently simulating mechanical and electrical faults in verilog-ams

Francesco Tosoni , Nicola Dall’Ora , Enrico Fraccaroli , and Franco Fummi

2022 Forum on Specification & Design Languages (FDL), 2022

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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.
The challenges of coupling digital-twins with multiple classes of faults

Francesco Tosoni , Nicola Dall’Ora , Enrico Fraccaroli , and Franco Fummi

2022 IEEE 23rd Latin American Test Symposium (LATS), 2022

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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.
Inferring mechanical fault models from the electrical domain

Nicola Dall’Ora , Francesco Tosoni , Enrico Fraccaroli , and Franco Fummi

2022 IEEE 5th International Conference on Industrial Cyber-Physical Systems (ICPS), 2022

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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.
Investigation on Realistic Stuck-on/off Defects to Complement IEEE P2427 Draft Standard

Sadia Azam , Nicola Dall’Ora , Enrico Fraccaroli , André Alberts , Renaud Gillon , and 1 more author

2022 23rd International Symposium on Quality Electronic Design (ISQED), 2022

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Historically, the concept of stuck-on/off defect did not originate from physical observations but rather to model the behavior of faults at the gate level. Digital stuck-at fault models where a transistor is considered frozen in on-state or off-state may not apply well on analog circuits because even a slight variation could create deviations of several magnitudes. This implies that standard stuck-at faults would not be general enough for analog behavior, i.e., the transistor will not be stuck to have dI/dVg = 0.0. Recent works have suggested to consider physical phenomena, like modeling oxide defects into the transistor, less abstract with respect to digital stuck-at fault and analog stuck-on/off defect.This paper focuses on evaluating faults proposed by the IEEE P2427 standard, which is still a work-in-progress standard. Moreover, a novel approach is presented for modeling realistic stuck-on/off defects based on oxide defects. We investigate the impact of these faults on the circuit on two designs taken from the IEEE analog-benchmarks circuit collection: an operational amplifier and a comparator model. Furthermore, we apply a novel method that relies on AC matrices extracted at several operating points and combines it with a circle-fitting technique to compare faults with uncertain parameters.
Functional Level Abstraction and Simulation of Verilog-AMS Piecewise Linear Models

Sadia Azam , Nicola Dall’Ora , Enrico Fraccaroli , and Franco Fummi

2022 23rd International Symposium on Quality Electronic Design (ISQED), 2022

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In electronic design and testing, the simulation speed of analog components is crucial. Moreover, the simulation of heterogeneous components embedded in a Virtual Platforms (VP) needs to be fast and accurate. Often, the analog components are non-linear, and simulating them is not easy to ensure the model’s convergence. In this context, techniques for simulating linear circuits are stable and efficient, but there are still many research gaps for non-linear circuits. There are no systematic methods available to solve non-linear equations efficiently. One standard method is to solve these non-linear equations by describing them as a piecewise linear (PWL) models. PWL techniques approximate non-linear functions with a set of linear functions. This is common to most solver methods: they linearize to compute an inverse matrix, finding which direction to move to satisfy the equations.In this article, an abstraction methodology for PWL models is proposed. By using this methodology, it is possible to abstract a piecewise model described with the Verilog-AMS language to the C++ language. These C++ models can be integrated into VPs. A half-wave rectifier and memristor model are selected to explain and validate the methodology. Furthermore, to show the effectiveness of the proposed technique, the abstracted model of the half-wave rectifier is integrated into a MEMS accelerometer. Moreover, the accelerometer is integrated into a VP to show the effectiveness of the functional simulation.

2021

Digital transformation of a production line: Network design, online data collection and energy monitoring

Nicola Dall’Ora , Khaled Alamin , Enrico Fraccaroli , Massimo Poncino , Davide Quaglia , and 1 more author

IEEE Transactions on Emerging Topics in Computing, 2021

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The concept of Industry 4.0 originates from the will to introduce the benefits of digital computation into new and existing industrial plants to save time, materials and energy. The digital transformation requires that all machinery of the production line are connected together and with the enterprise applications, to capture and analyze data across all manufacturing stages. Then, such collected data can be exploited to take strategic decision on the production and to monitor it, reacting to unexpected behaviors and thus reducing downtime and maintenance costs. This article aims at supporting production engineers approaching digital transformation by exemplifying its key elements on a real life scenario, the Industrial Computer Engineering laboratory of the University of Verona. First of all, the article discusses network design, as communication is an enabler of the other technologies. Network is realized through automatic network synthesis from requirements and characteristics of the production line data flow. Then, the paper discusses data collection and the construction of a digital twin monitoring power consumption of the production line, with the goal of detecting any discrepancy between real time data and digital twin data. This allows to trigger an early intervention on the line, to guarantee an effective maintenance.
A common manipulation framework for transistor-level languages

Nicola Dall’Ora , Sadia Azam , Enrico Fraccaroli , André Alberts , and Franco Fummi

2021 Forum on specification & Design Languages (FDL), 2021

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There are plentiful successors of SPICE language for describing transistor-level designs. For most of them, the semantic matches those of SPICE, and only the syntax is changed. Others instead provide more default models or analysis tools. Consequently, a commercial tool is usually required for simulating, analyzing, and especially manipulating these languages. This article proposes a framework that relies on the shared semantic for reading, writing, or manipulating transistor-level designs. The ultimate goal of the framework is: reading an input design written in a specific syntax and then allowing to write the same design in another syntax. First, the input description is parsed by a language-specific front-end which turns it into an in-memory abstract syntax tree that follows the common semantic. Then, the in-memory description can be subject to different user-defined manipulations built on top of a series of API or visitor/listener classes. Finally, the description goes through the desired back-end, transforming the in-memory description into the target transistor-level language. As a use-case for the proposed framework, we chose the process of analog fault injection. This activity requires adding, removing, or replacing nodes, components, or even entire sub-circuits. Therefore, the framework is completely written in C++, and its APIs are also interfaced with python. The entire framework is open-source and available on GitHub.
Multi-Discipline Fault Modeling with Verilog-AMS

Nicola Dall’Ora , Enrico Fraccaroli , Sara Vinco , Franco Fummi , and others

IEEE International Conference on Industrial Cyber-Physical Systems (ICPS), 2021

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Constructing a simulable model of a production line is crucial to ensure adequate maintenance, but it is nonetheless too complex due to the presence of highly heterogeneous components. In this perspective, Verilog-AMS is a promising solution, as it allows to cover different levels of details, from transistor-level and digital components to multi-physical dynamics. This paper shows how Verilog-AMS can be used to model production line components by exploiting multiple disciplines effectively. Furthermore, we will prove that Verilog-AMS allows efficient modeling of faults by inserting saboteurs and mutants in multi-physics descriptions. This methodology allows the definition of a multi-discipline fault injection technique that can be used to generate valuable data to support any analysis based on faulty temporal series, like predictive maintenance.
Predictive fault grouping based on faulty ac matrices

Nicola Dall’Ora , Sadia Azam , Enrico Fraccaroli , André Alberts , and Franco Fummi

2021 24th International Symposium on Design and Diagnostics of Electronic Circuits & Systems (DDECS), 2021

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In this article, a predictive fault grouping based on the collection of faulty AC matrices at fault-free operating points is presented as a means to approximate the final distribution of faults in equivalence classes using a minimal computational effort. The method is computationally cheap because it avoids performing DC or transient simulations with faults injected and limits itself only to AC simulations with faults activated. The technique provides an approximation, since it does not characterize faults at the corresponding faulty operating point but instead looks at how they would modify the fault-free operating point once injected.The approximate grouping achieves an excellent correlation to the final classification based on the comparison of faulty transient wave-forms. It is not meant as a substitute for the traditional fault injection simulations but as a support to decision making. It allows prioritizing faults to characterize the possible failure modes with a minimum number of fault injections, pushing out fault injections which are estimated to marginally increase the learning.
Digital twin extension with extra-functional properties

Khaled Alamin , Sara Vinco , Massimo Poncino , Nicola Dall’Ora , Enrico Fraccaroli , and 1 more author

2021 Design, Automation & Test in Europe Conference & Exhibition (DATE), 2021

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Digital twins of production lines do not focus solely on the management of the production process, they can also monitor and optimize other extra-functional aspects such as energy consumption and communications. This paper proposes the extension of digital twin concept in such directions. First, we extend the digital twin with models of energy consumption, that allow the monitoring of production line components throughout production lifetime. Then, we propose a flow to design the communication network starting from information obtained from the digital twin concerning the production, usage and flowing of information through the plant. All these methodologies start from the production line specification, then they enrich it with data collected during operation, and finally information is used to perform design and optimization. Results have been shown on a real Industry 4.0 research facility.

2020

The design of a digital-twin for predictive maintenance

Stefano Centomo , Nicola Dall’Ora , and Franco Fummi

2020 25th IEEE International Conference on Emerging Technologies and Factory Automation (ETFA), 2020

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Predictive maintenance in a manufacturing company is strategic, in order to maintain high production quality and to avoid unexpected production downtimes. In this scenario, the prediction of future machineries health status is necessary in order to plan maintenance cycles and to optimize the production. The proposed approach relies on the use of Electronic Design Automation (EDA) techniques mapped from the electronic domain to the production line domain. This paper proposes a general framework based on the EDA approach that allows to set-up a maintenance strategy by analyzing data retrieved from sensors. An MSM, is associated to each observable measurement, while a Supervisor monitors the current state of each Monitoring State Machine (MSM) by raising alerts when the monitored equipment is deviating from its normal behavior. This framework is the Digital-Twin of the plant devoted to its monitoring. It has some execution modalities ranging from online monitoring to predictive maintenance. The methodology has been applied to a mechanical transmission system showing its effectiveness.
Functionality and fault modeling of a dc motor with verilog-ams

Nicola Dall’Ora , Sara Vinco , and Franco Fummi

2020 IEEE 18th International Conference on Industrial Informatics (INDIN), 2020

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In the context of industry 4.0, it is strategic to support factories with innovative maintenance approaches, so to avoid faults and decrease the risks of a production stop. The first step of the digitization of factories has been the collection of large amounts of data monitoring the health status of the plant. However, such data is of little use unless it is clearly correlated with information about faults occurred on the line: some faults may be sporadic, or happen only in extremely critical conditions, and thus no data may be available related to their occurrence. Artificially generating such data would force to actually damage the plant, that is of course not a viable solution. The goal of this work is to generate faulty temporal series, that reproduce the behavior of a component on the occurrence of specific faults. The innovative approach models the component of interest in Verilog-AMS (VAMS) and systematically injects the faults of interest, by keeping a direct link with the real possible cause of such faulty behavior on the plant. To prove the effectiveness of the proposed solution, the approach is applied to a direct current motor (DC motor), an electromechanical system that converts electrical energy into mechanical energy.

2019

Industrial-iot data analysis exploiting electronic design automation techniques

Nicola Dall’Ora , Stefano Centomo , and Franco Fummi

2019 IEEE 8th International Workshop on Advances in Sensors and Interfaces (IWASI), 2019

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Predictive maintenance is a strategic activity in the context of Industry 4.0 in order to maintain a certain level of quality production and to avoid unexpected equipment downtimes. In this scenario, the analysis of IIOT data is necessary to achieve prediction on the future machinery’ status. The proposed approach relies on the use of Electronic Design Automation (EDA) techniques mapped from electronic domain to production line domain. These EDA techniques are combined with field knowledge, especially for Predictive Maintenance analysis. This presentation describes a methodology that allows to abstract raw data retrieved from IIOT sensors into a class of severity levels, core of the proposed methodology. For example, a class of severity level is reported in the ISO 10816 standard for vibration measurement, but similar concepts are proposed for other values. The methodology consists of two phases: first of all, traces of the nominal behavior are stored to be reused, then, such raw data are filtered with the nominal behavior and translated into severity levels. Such levels are then embedded into IIoT edge devices through the synthesis of the so-called Predictive Maintenance State Machines. The methodology has been validated on the model of a mechanical transmission system. Furthermore, the correctness of the strategy has been proved by injecting faults on the original model and by exploiting simulation procedures under different operational scenarios. This methodology gives to IIoT sensors their specific role in the software automation pyramid, by abstracting their data into levels used through the formalism of Predictive Maintenance State Machines (PMSM).

2018

A framework for the design and simulation of embedded vision applications based on OpenVX and ROS

Stefano Aldegheri , Nicola Bombieri , Nicola Dall’Ora , Franco Fummi , Simone Girardi , and 1 more author

2018 IEEE International Symposium on Circuits and Systems (ISCAS), 2018

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Customizing computer vision applications for embedded systems is a common and widespread problem in the cyber-physical systems community. Such a customization means parametrizing the algorithm by considering the external environment and mapping the Software application to the heterogeneous Hardware resources by satisfying non-functional constraints like performance, power, and energy consumption. This work presents a framework for the design and simulation of embedded vision applications that integrates the OpenVX standard platform with the Robot Operating System (ROS). The paper shows how the framework has been applied to tune the ORB-SLAM application for an NVIDIA Jetson TX2 board by considering different environment contexts and different design constraints.