A novel throughput control algorithm for semi-heterarchical industry 4.0 architecture

Modern market scenarios are imposing a radical change in the production concept, driving companies’ attention to customer satisfaction through increased product customization and quick response strategies to maintain competitiveness. At the same time, the growing development of Industry 4.0 technologies made possible the creation of new manufacturing paradigms in which an increased level of autonomy is one of the key concepts to consider. Taking the advantage from the recent development around the semi-heterarchical architecture, this work proposes a first model for the throughput control of a production system managed by such an architecture. A cascade control algorithm is proposed considering work-in-progress (WIP) as the primary control lever for achieving a specific throughput target. It is composed of an optimal control law based on an analytical model of the considered production system, and of a secondary proportional-integral-derivative controller capable of performing an additional control action that addresses the error raised by the theoretical model’s. The proposed throughput control algorithm has been tested in different simulated scenarios, and the results showed that the combination of the control actions made it possible to have continuous adjustment of the WIP of the controlled production system, maintaining it at the minimum value required to achieve the requested throughput with nearly zero errors

Complex scheduling models and analyses for property-based real-time embedded systems

Modern multi core architectures and parallel applications pose a significant challenge to the worst-case centric real-time system verification and design efforts. The involved model and parameter uncertainty contest the fidelity of formal real-time analyses, which are mostly based on exact model assumptions. In this dissertation, various approaches that can accept parameter and model uncertainty are presented. In an attempt to improve predictability in worst-case centric analyses, the exploration of timing predictable protocols are examined for parallel task scheduling on multiprocessors and network-on-chip arbitration. A novel scheduling algorithm, called stationary rigid gang scheduling, for gang tasks on multiprocessors is proposed. In regard to fixed-priority wormhole-switched network-on-chips, a more restrictive family of transmission protocols called simultaneous progression switching protocols is proposed with predictability enhancing properties. Moreover, hierarchical scheduling for parallel DAG tasks under parameter uncertainty is studied to achieve temporal- and spatial isolation. Fault-tolerance as a supplementary reliability aspect of real-time systems is examined, in spite of dynamic external causes of fault. Using various job variants, which trade off increased execution time demand with increased error protection, a state-based policy selection strategy is proposed, which provably assures an acceptable quality-of-service (QoS). Lastly, the temporal misalignment of sensor data in sensor fusion applications in cyber-physical systems is examined. A modular analysis based on minimal properties to obtain an upper-bound for the maximal sensor data time-stamp difference is proposed

Stability analysis and robust control of power networks in stochastic environment

The modern power grid is moving towards a cleaner form of energy, renewable energy to meet the ever-increasing demand and new technologies are being installed in the power network to monitor and maintain a stable operation. Further, the interactions in the network are not anymore localized but take place over a system, and the control centers are located remotely, thus involving control of network components over communication channels. Further, given the rapid integration of wind energy, it is essential to study the impact of wind variability on the system stability and frequency regulation. Hence, we model the unreliable and intermittent nature of wind energy with stochastic uncertainty. Moreover, the phasor measurement unit (PMU) data from the power network is transmitted to the control center over communication channels, and it is susceptible to inherent communication channel uncertainties, cyber attacks, and hence, the data at the receiving end cannot be accurate. In this work, we model these communication channels with stochastic uncertainties to study the impact of stochastic uncertainty on the stability and wide area control of power network. The challenging aspect of the stability analysis of stochastic power network is that the stochastic uncertainty appears multiplicative as well as additive in the system dynamics. The notion of mean square exponential stability is considered to study the properties of stochastic power network expressed as a networked control system (NCS) with stochastic uncertainty. We develop, necessary and sufficient conditions for mean square exponential stability which are shown in terms of the input-output property of deterministic or nominal system dynamics captured by the mean square system norm and variance of the channel uncertainty. For a particular case of single input channel uncertainty, we also prove a fundamental limitation result that arises in the mean square exponential stabilization of the continuous-time linear system. Overall, the theoretical contributions in this work generalize the existing results on stability analysis from discrete-time linear systems to continuous-time linear systems with multiplicative uncertainty. The stability results can also be interpreted as a small gain theorem for continuous-time stochastic systems. Linear Matrix Inequalities (LMI)-based optimization formulation is provided for the computation of mean square system norm for stability analysis and controller synthesis. An IEEE 68 bus system is considered, and the fragility of the decentralized load-side primary frequency controller with uncertain wind is shown. The critical variance value is shown to decrease with the increase in the cost of the controllable loads and with the rise in penetration of wind farms. Next, we model the power network with detailed higher order differential equations for synchronous generator (SG), wind turbine generator (WTG). The network power flow equations are expressed as algebraic equations. The resultant system is described by a detailed higher order nonlinear differential-algebraic model. It is shown that the uncertainty in the wind speed appears multiplicative in the system dynamics. Stochastic stability of such systems is characterized based on the developed results on mean square exponential stability. In particular, we study the stochastic small signal stability of the resultant system and characterize the critical variance in wind speeds, beyond which the grid dynamics becomes mean square unstable. The power fluctuations in the demand side and intermittent generation (from renewables) cause frequency excursions from the nominal value. In this context, we consider the controllable loads which can vary their power to achieve frequency regulation based on the frequency feedback from the network. Two different load-side frequency controller strategies, decentralized and distributed frequency controllers are studied in the presence of stochastic wind. Finally, the time-domain simulations on an IEEE 39 bus system (by replacing some of the traditional SGs with WTG) are shown using the wind speeds modeled as stochastic as well as actual wind speeds obtained from the wind farm located near Ames, Iowa. It can be seen that, with an increase in the penetration of wind generation in the network, the network turns mean square unstable. Furthermore, we capture the mean square unstable behavior of the power network with increased penetration of renewables using the statistics of actual wind analytically and complement them through linear and nonlinear time domain simulations. Finally, we analyze the vulnerability of communication channel to stochastic uncertainty on an IEEE 39 bus system and design a wide area controller that is robust to various sources of uncertainties that arise in the communication channels. Further, the PMU measurements and wide area control inputs are rank ordered based on their criticality

Analysis and Evaluation of the Impacts of Predictive Analytics on Production System Performances in the Semiconductor Industry

Problem Statement: Predictive Analytics (PA) may effectively support semiconductor industry (SI) companies in order to manage the special challenges in SI value chains. To discover the implications of PA, the realistic benefits as well as its limitations of its application to semiconductor manufacturing, it is necessary to assess in which ways the application of PA affects the production system (PS) performances. However, based on the literature survey, the influences of PA on the various performance characteristics of an SI PS are not as clear as expected for the efficiently operative application. Besides, the existing performance models are not effective to predict the impacts of PA on the SI PS performances. Therefore, the overall aim of this thesis is to analyse and evaluate the impacts of PA on the SI PS performances and to identify under which conditions a PA application would generate the most significant performance improvements. The focus of this thesis is predictive maintenance (PdM). Research Methodology: Based on a post-positivist philosophy, the thesis applies a deductive research approach using mixed-methods for data collection. The research design has the following stages: (1) theory, (2) hypothesis, (3) state of research, (4) case study and (5) verification. Main Achievements: (1) The systematic literature review is carried out to identify the gaps of the existing research and based on these findings, a conceptual framework is proposed and developed. (2) The existing performance models are analysed and evaluated against their applicability to this study. (3) A causal loop model for SI PS is generated based on the assessment of experts with industrial engineering and equipment maintenance expertise. (4) An expert system is developed and evaluated in order to investigate transitive and contradictory effects of PdM on SI PS performances. (5) A simulation model is developed and validated for investigating the strengths and limitations of PdM regarding SI PS performances under different circumstances. Results: The results of the logical inference study show that PdM has 34 positive effects as well as 4 contradictory effects on SI PS performance characteristics. Based on the various simulation experiments, it has been found that (1) ’Mean Time to Repair’ decreases only if PdM supports proportionate reduction of failures and repair times. (2) Logistics performance improves only if the underlying workcenter is limited in capacity or the four partners are nonsynchronous. (3) PdM supports optimal cost decreases for workcenters where the degree of exhausting wear limits can be most effectively improved and (4) the degree of yield improvement gained by PdM is dependent on the operation scrap rate. However, (5) if a workcenter has overcapacity, PdM will potentially worsen PS performances, even if the particular workcenter performance can be improved. These new insights advance existing knowledge in production managements when adopting predictive technologies at SI PS in order to improve PS performances. The findings above enable SI practitioners to justify a PdM investment and to select suitable workcenters in order to improve SI PS performances by applying the proposed PdM. Contributions: The main contributions of this PhD project can be divided into practical application and theoretical work. The contributions from the theoretical perspective are: 1) The critical review and evaluation of the state of the research for PA in the context of semiconductor manufacturing and the models for predicting and evaluating SI PS performances. 2) A new framework for investigating the implications of PA on the challenges such as gaining high utilizations and controlling the variability in production processes in SI value chains. 3) The new knowledge about transitive and contradictory effects of PdM on SI PS performances, which indicates that PdM can be used to improve PS performances beyond a single machine. 4) The new knowledge about strengths and limitations of PdM in order to improve SI PS performances under particular circumstances. The contributions from the practical application perspective are: 1) A practical method for identifying workcenters where PdM delivers the most significant benefits for SI PS performances. 2) An expert system that provides a comprehensive knowledge base about causes and effects within SI PS in order to justify a PdM investment. 3) A concise review of important PA applications, their capabilities for the wafer fabrication and the most suited PA methods. These findings can be adopted by SI practitioners

Computer Aided Verification

This open access two-volume set LNCS 13371 and 13372 constitutes the refereed proceedings of the 34rd International Conference on Computer Aided Verification, CAV 2022, which was held in Haifa, Israel, in August 2022. The 40 full papers presented together with 9 tool papers and 2 case studies were carefully reviewed and selected from 209 submissions. The papers were organized in the following topical sections: Part I: Invited papers; formal methods for probabilistic programs; formal methods for neural networks; software Verification and model checking; hyperproperties and security; formal methods for hardware, cyber-physical, and hybrid systems. Part II: Probabilistic techniques; automata and logic; deductive verification and decision procedures; machine learning; synthesis and concurrency. This is an open access book

A Conceptual Model for Quality 4.0 Deployment in U.S. Based Manufacturing Firms

Manufacturing is currently undergoing a fourth industrial revolution, referred to as Industry 4.0, enabled by digital technologies and advances in our ability to collect and use data. Quality 4.0 is the application of Industry 4.0 to enhance the quality function within an organization. Quality practitioners are uniquely positioned within organizations and already possess data application skillsets. Despite a perception that Quality 4.0 will be critical to future success shared by a majority of industry, most companies have not attempted to implement Quality 4.0 strategy, and those that have report very low rates of success. The goal of this study was to understand the challenges and key factors behind implementation of a Quality 4.0 system and develop a model for implementation, highlighting those key factors. The model was developed through literature review, case study analysis, and expert interviews. The model indicated that four main constructs exist in Quality 4.0 deployment, digital strategy, enabling factors, methodologies, and technology. A top-level strategy should be developed to address key technology development themes as well as nontechnical business process themes. Strategy should then be executed in the domain of enabling factors and methodologies with a clear technology application serving as the output. A successful Quality 4.0 implementation will use the technology application to drive tangible quality improvement activities which add value to the business

Industry 4.0: product digital twins for remanufacturing decision-making

Currently there is a desire to reduce natural resource consumption and expand circular business principles whilst Industry 4.0 (I4.0) is regarded as the evolutionary and potentially disruptive movement of technology, automation, digitalisation, and data manipulation into the industrial sector. The remanufacturing industry is recognised as being vital to the circular economy (CE) as it extends the in-use life of products, but its synergy with I4.0 has had little attention thus far. This thesis documents the first investigating into I4.0 in remanufacturing for a CE contributing a design and demonstration of a model that optimises remanufacturing planning using data from different instances in a product’s life cycle. The initial aim of this work was to identify the I4.0 technology that would enhance the stability in remanufacturing with a view to reducing resource consumption. As the project progressed it narrowed to focus on the development of a product digital twin (DT) model to support data-driven decision making for operations planning. The model’s architecture was derived using a bottom-up approach where requirements were extracted from the identified complications in production planning and control that differentiate remanufacturing from manufacturing. Simultaneously, the benefits of enabling visibility of an asset’s through-life health were obtained using a DT as the modus operandi. A product simulator and DT prototype was designed to use Internet of Things (IoT) components, a neural network for remaining life estimations and a search algorithm for operational planning optimisation. The DT was iteratively developed using case studies to validate and examine the real opportunities that exist in deploying a business model that harnesses, and commodifies, early life product data for end-of-life processing optimisation. Findings suggest that using intelligent programming networks and algorithms, a DT can enhance decision-making if it has visibility of the product and access to reliable remanufacturing process information, whilst existing IoT components provide rudimentary “smart” capabilities, but their integration is complex, and the durability of the systems over extended product life cycles needs to be further explored

13th International Conference on Modeling, Optimization and Simulation - MOSIM 2020

Comité d’organisation: Université Internationale d’Agadir – Agadir (Maroc) Laboratoire Conception Fabrication Commande – Metz (France)Session RS-1 “Simulation et Optimisation” / “Simulation and Optimization” Session RS-2 “Planification des Besoins Matières Pilotée par la Demande” / ”Demand-Driven Material Requirements Planning” Session RS-3 “Ingénierie de Systèmes Basées sur les Modèles” / “Model-Based System Engineering” Session RS-4 “Recherche Opérationnelle en Gestion de Production” / "Operations Research in Production Management" Session RS-5 "Planification des Matières et des Ressources / Planification de la Production” / “Material and Resource Planning / Production Planning" Session RS-6 “Maintenance Industrielle” / “Industrial Maintenance” Session RS-7 "Etudes de Cas Industriels” / “Industrial Case Studies" Session RS-8 "Données de Masse / Analyse de Données” / “Big Data / Data Analytics" Session RS-9 "Gestion des Systèmes de Transport” / “Transportation System Management" Session RS-10 "Economie Circulaire / Développement Durable" / "Circular Economie / Sustainable Development" Session RS-11 "Conception et Gestion des Chaînes Logistiques” / “Supply Chain Design and Management" Session SP-1 “Intelligence Artificielle & Analyse de Données pour la Production 4.0” / “Artificial Intelligence & Data Analytics in Manufacturing 4.0” Session SP-2 “Gestion des Risques en Logistique” / “Risk Management in Logistics” Session SP-3 “Gestion des Risques et Evaluation de Performance” / “Risk Management and Performance Assessment” Session SP-4 "Indicateurs Clés de Performance 4.0 et Dynamique de Prise de Décision” / ”4.0 Key Performance Indicators and Decision-Making Dynamics" Session SP-5 "Logistique Maritime” / “Marine Logistics" Session SP-6 “Territoire et Logistique : Un Système Complexe” / “Territory and Logistics: A Complex System” Session SP-7 "Nouvelles Avancées et Applications de la Logique Floue en Production Durable et en Logistique” / “Recent Advances and Fuzzy-Logic Applications in Sustainable Manufacturing and Logistics" Session SP-8 “Gestion des Soins de Santé” / ”Health Care Management” Session SP-9 “Ingénierie Organisationnelle et Gestion de la Continuité de Service des Systèmes de Santé dans l’Ere de la Transformation Numérique de la Société” / “Organizational Engineering and Management of Business Continuity of Healthcare Systems in the Era of Numerical Society Transformation” Session SP-10 “Planification et Commande de la Production pour l’Industrie 4.0” / “Production Planning and Control for Industry 4.0” Session SP-11 “Optimisation des Systèmes de Production dans le Contexte 4.0 Utilisant l’Amélioration Continue” / “Production System Optimization in 4.0 Context Using Continuous Improvement” Session SP-12 “Défis pour la Conception des Systèmes de Production Cyber-Physiques” / “Challenges for the Design of Cyber Physical Production Systems” Session SP-13 “Production Avisée et Développement Durable” / “Smart Manufacturing and Sustainable Development” Session SP-14 “L’Humain dans l’Usine du Futur” / “Human in the Factory of the Future” Session SP-15 “Ordonnancement et Prévision de Chaînes Logistiques Résilientes” / “Scheduling and Forecasting for Resilient Supply Chains

Stochastic Model Predictive Control for Eco-Driving Assistance Systems in Electric Vehicles

Electric vehicles are expected to become one of the key elements of future sustainable transportation systems. The first generation of electric cars are already commercially available but still, suffer from problems and constraints that have to be solved before a mass market might be created. Key aspects that will play an important role in modern electric vehicles are range extension, energy efficiency, safety, comfort as well as communication. An overall solution approach to integrating all these aspects is the development of advanced driver assistance systems to make electric vehicles more intelligent. Driver assistance systems are based on the integration of suitable sensors and actuators as well as electronic devices and software-enabled control functionality to automatically support the human driver. Driver assistance for electric vehicles will differ from the already used systems in fuel-powered cars such as electronic stability programs, adaptive cruise control etc. in a way that they must support energy efficiency while the system itself must also have a low power consumption. In this work, an eco-driving functionality as the first step towards those new driver assistance systems for electric vehicles will be investigated. Using information about the internal state of the car, navigation information as well as advanced information about the environment coming from sensors and network connections, an algorithm will be developed that will adapt the speed of the vehicle automatically to minimize energy consumption. From an algorithmic point of view, a stochastic model predictive control approach will be applied and adapted to the special constraints of the problem. Finally, the solution will be tested in simulations as well as in first experiments with a commercial electric vehicle in the SnT Automation & Robotics Research Group (SnT ARG)

Service-oriented architecture for device lifecycle support in industrial automation

Dissertação para obtenção do Grau de Doutor em Engenharia Electrotécnica e de Computadores Especialidade: Robótica e Manufactura IntegradaThis thesis addresses the device lifecycle support thematic in the scope of service oriented industrial automation domain. This domain is known for its plethora of heterogeneous equipment encompassing distinct functions, form factors, network interfaces, or I/O specifications supported by dissimilar software and hardware platforms. There is then an evident and crescent need to take every device into account and improve the agility performance during setup, control, management, monitoring and diagnosis phases. Service-oriented Architecture (SOA) paradigm is currently a widely endorsed approach for both business and enterprise systems integration. SOA concepts and technology are continuously spreading along the layers of the enterprise organization envisioning a unified interoperability solution. SOA promotes discoverability, loose coupling, abstraction, autonomy and composition of services relying on open web standards – features that can provide an important contribution to the industrial automation domain. The present work seized industrial automation device level requirements, constraints and needs to determine how and where can SOA be employed to solve some of the existent difficulties. Supported by these outcomes, a reference architecture shaped by distributed, adaptive and composable modules is proposed. This architecture will assist and ease the role of systems integrators during reengineering-related interventions throughout system lifecycle. In a converging direction, the present work also proposes a serviceoriented device model to support previous architecture vision and goals by including embedded added-value in terms of service-oriented peer-to-peer discovery and identification, configuration, management, as well as agile customization of device resources. In this context, the implementation and validation work proved not simply the feasibility and fitness of the proposed solution to two distinct test-benches but also its relevance to the expanding domain of SOA applications to support device lifecycle in the industrial automation domain