A comparative study of three model-based FDI approaches for Discrete Event Systems

6 pagesInternational audienceIn this paper three model-based Fault Detection and Isolation (FDI) approaches for Discrete Event Systems (DES) are evaluated. The considered approaches are the diagnoser approach, the templates approach and the residual approach. The investigated methods have different characteristics like timed / non-timed behavior and fault-free / faulty system models with important impacts on the model-building process and the respective effectiveness. By applying the three methods to the same benchmark system, their respective performances are analyzed in terms of fault detection and fault isolation ability, complexity of implementation and avoidance of false alarms

Application of Advanced Early Warning Systems with Adaptive Protection

This project developed and field-tested two methods of Adaptive Protection systems utilizing synchrophasor data. One method detects conditions of system stress that can lead to unintended relay operation, and initiates a supervisory signal to modify relay response in real time to avoid false trips. The second method detects the possibility of false trips of impedance relays as stable system swings “encroach” on the relays’ impedance zones, and produces an early warning so that relay engineers can re-evaluate relay settings. In addition, real-time synchrophasor data produced by this project was used to develop advanced visualization techniques for display of synchrophasor data to utility operators and engineers

A Modeling and Analysis Framework To Support Monitoring, Assessment, and Control of Manufacturing Systems Using Hybrid Models

The manufacturing industry has constantly been challenged to improve productivity, adapt to continuous changes in demand, and reduce cost. The need for a competitive advantage has motivated research for new modeling and control strategies able to support reconfiguration considering the coupling between different aspects of plant floor operations. However, models of manufacturing systems usually capture the process flow and machine capabilities while neglecting the machine dynamics. The disjoint analysis of system-level interactions and machine-level dynamics limits the effectiveness of performance assessment and control strategies. This dissertation addresses the enhancement of productivity and adaptability of manufacturing systems by monitoring and controlling both the behavior of independent machines and their interactions. A novel control framework is introduced to support performance monitoring and decision making using real-time simulation, anomaly detection, and multi-objective optimization. The intellectual merit of this dissertation lies in (1) the development a mathematical framework to create hybrid models of both machines and systems capable of running in real-time, (2) the algorithms to improve anomaly detection and diagnosis using context-sensitive adaptive threshold limits combined with context-specific classification models, and (3) the construction of a simulation-based optimization strategy to support decision making considering the inherent trade-offs between productivity, quality, reliability, and energy usage. The result is a framework that transforms the state-of-the-art of manufacturing by enabling real-time performance monitoring, assessment, and control of plant floor operations. The control strategy aims to improve the productivity and sustainability of manufacturing systems using multi-objective optimization. The outcomes of this dissertation were implemented in an experimental testbed. Results demonstrate the potential to support maintenance actions, productivity analysis, and decision making in manufacturing systems. Furthermore, the proposed framework lays the foundation for a seamless integration of real systems and virtual models. The broader impact of this dissertation is the advancement of manufacturing science that is crucial to support economic growth. The implementation of the framework proposed in this dissertation can result in higher productivity, lower downtime, and energy savings. Although the project focuses on discrete manufacturing with a flow shop configuration, the control framework, modeling strategy, and optimization approach can be translated to job shop configurations or batch processes. Moreover, the algorithms and infrastructure implemented in the testbed at the University of Michigan can be integrated into automation and control products for wide availability.PHDMechanical EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttps://deepblue.lib.umich.edu/bitstream/2027.42/147657/1/migsae_1.pd

IMPROVE - Innovative Modelling Approaches for Production Systems to Raise Validatable Efficiency

This open access work presents selected results from the European research and innovation project IMPROVE which yielded novel data-based solutions to enhance machine reliability and efficiency in the fields of simulation and optimization, condition monitoring, alarm management, and quality prediction

From centralized to decentralized approach for optimal Controller of Discrete Manufacturing Systems

International audienceThis paper deals with a comparison of centralized and decentralized approaches to obtain an optimal controller for discrete manufacturing systems. It is based on a modular modeling of the plant to avoid combinatory explosion found in centralized structure and a synthesis algorithm. From the local Plant Elements, local constraints are defined to build local supervisors. Local constraints restrict the system behavior within a desired specification. Global constraints are added to establish high level supervisors. The resulting automata are translated in a normalized language for implementation in a Programmable Logic Controller

Science Hackathons for Cyberphysical System Security Research: Putting CPS testbed platforms to good use

A challenge is to develop cyber-physical system scenarios that reflect the diversity and complexity of real-life cyber-physical systems in the research questions that they address. Time-bounded collaborative events, such as hackathons, jams and sprints, are increasingly used as a means of bringing groups of individuals together, in order to explore challenges and develop solutions. This paper describes our experiences, using a science hackathon to bring individual researchers together, in order to develop a common use-case implemented on a shared CPS testbed platform that embodies the diversity in their own security research questions. A qualitative study of the event was conducted, in order to evaluate the success of the process, with a view to improving future similar events

A PLC Variable Identification Method by Manual Declaration of Time-Stamped Events

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Collision Prevention In Operation-Synchronized Simulations Using Dynamic Prescheduling Of Simulation Parameters

The increasing use of simulation technologies, especially virtual commissioning, in the context of modern plant development for manufacturing discrete parts is driven by the pressure to shorten time-to-market cycles and overcome supply bottlenecks. The need for robust technologies to seamlessly integrate the digital and physical world is growing as machine data becomes more readily available. A challenge to this integration is presented by the need to continuously adjust the movement parameters, especially for event-discrete actuators based on live data, taking wear, ageing and process-time fluctuations into account. A lack of synchronization leads to discrepancies between the simulation and reality renders them useless. Related works in this field are discussed, which highlight the complexities of achieving synchronization between simulation and reality, particularly in event-discrete signals and systems. The aim of this article is to present a method for reusing virtual commissioning models for operation-synchronized simulations at actuator level. This approach includes introducing of a methodology called prescheduling in order to compensate process uncertainties and also defines the necessary requirements for the simulation tool and model. The method is validated using an industrial test system and a commercial virtual commissioning tool to confirm its suitability for real-life implementation in industrial plants, which suggests its suitability for improving production efficiency and reducing costs by means of machine monitoring and proactive control interventions