Fault-based Analysis of Industrial Cyber-Physical Systems

The fourth industrial revolution called Industry 4.0 tries to bridge the gap between traditional Electronic Design Automation (EDA) technologies and the necessity of innovating in many indus- trial fields, e.g., automotive, avionic, and manufacturing. This complex digitalization process in- volves every industrial facility and comprises the transformation of methodologies, techniques, and tools to improve the efficiency of every industrial process. The enhancement of functional safety in Industry 4.0 applications needs to exploit the studies related to model-based and data-driven anal- yses of the deployed Industrial Cyber-Physical System (ICPS). Modeling an ICPS is possible at different abstraction levels, relying on the physical details included in the model and necessary to describe specific system behaviors. However, it is extremely complicated because an ICPS is com- posed of heterogeneous components related to different physical domains, e.g., digital, electrical, and mechanical. In addition, it is also necessary to consider not only nominal behaviors but even faulty behaviors to perform more specific analyses, e.g., predictive maintenance of specific assets. Nevertheless, these faulty data are usually not present or not available directly from the industrial machinery. To overcome these limitations, constructing a virtual model of an ICPS extended with different classes of faults enables the characterization of faulty behaviors of the system influenced by different faults. In literature, these topics are addressed with non-uniformly approaches and with the absence of standardized and automatic methodologies for describing and simulating faults in the different domains composing an ICPS. This thesis attempts to overcome these state-of-the-art gaps by proposing novel methodologies, techniques, and tools to: model and simulate analog and multi-domain systems; abstract low-level models to higher-level behavioral models; and monitor industrial systems based on the Industrial Internet of Things (IIOT) paradigm. Specifically, the proposed contributions involve the exten- sion of state-of-the-art fault injection practices to improve the ICPSs safety, the development of frameworks for safety operations automatization, and the definition of a monitoring framework for ICPSs. Overall, fault injection in analog and digital models is the state of the practice to en- sure functional safety, as mentioned in the ISO 26262 standard specific for the automotive field. Starting from state-of-the-art defects defined for analog descriptions, new defects are proposed to enhance the IEEE P2427 draft standard for analog defect modeling and coverage. Moreover, dif- ferent techniques to abstract a transistor-level model to a behavioral model are proposed to speed up the simulation of faulty circuits. Therefore, unlike the electrical domain, there is no extensive use of fault injection techniques in the mechanical one. Thus, extending the fault injection to the mechanical and thermal fields allows for supporting the definition and evaluation of more reliable safety mechanisms. Hence, a taxonomy of mechanical faults is derived from the electrical domain by exploiting the physical analogies. Furthermore, specific tools are built for automatically instru- menting different descriptions with multi-domain faults. The entire work is proposed as a basis for supporting the creation of increasingly resilient and secure ICPS that need to preserve functional safety in any operating context

A Framework for the Design and Simulation of Embedded Vision Applications Based on OpenVX and ROS

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

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

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

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 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

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

The Design of a Digital-Twin for Predictive Maintenance

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

Industrial-IoT Data Analysis Exploiting Electronic Design Automation Techniques

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)

Functionality and Fault Modeling of a DC Motor with Verilog-AMS

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

Functional Level Abstraction and Simulation of Verilog-AMS Piecewise Linear Models

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

Multi-Discipline Fault Modeling with Verilog-AMS

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