3,851 research outputs found
FAST : a fault detection and identification software tool
The aim of this work is to improve the reliability and safety of complex critical control systems by contributing to the systematic application of fault diagnosis. In order to ease the utilization of fault detection and isolation (FDI) tools in the industry, a systematic approach is required to allow the process engineers to analyze a system from this perspective. In this way, it should be possible to analyze this system to find if it provides the required fault diagnosis and redundancy according to the process criticality. In addition, it should be possible to evaluate what-if scenarios by slightly modifying the process (f.i. adding sensors or changing their placement) and evaluating the impact in terms of the fault diagnosis and redundancy possibilities.
Hence, this work proposes an approach to analyze a process from the FDI perspective and for this purpose provides the tool FAST which covers from the analysis and design phase until the final FDI supervisor implementation in a real process. To synthesize the process information, a very simple format has been defined based on XML. This format provides the needed information to systematically perform the Structural Analysis of that process. Any process can be analyzed, the only restriction is that the models of the process components need to be available in the FAST tool. The processes are described in FAST in terms of process variables, components and relations and the tool performs the structural analysis of the process obtaining: (i) the structural matrix, (ii) the perfect matching, (iii) the analytical redundancy relations (if any) and (iv) the fault signature matrix.
To aid in the analysis process, FAST can operate stand alone in simulation mode allowing the process engineer to evaluate the faults, its detectability and implement changes in the process components and topology to improve the diagnosis and redundancy capabilities. On the other hand, FAST can operate on-line connected to the process plant through an OPC interface. The OPC interface enables the possibility to connect to almost any process which features a SCADA system for supervisory control. When running in on-line mode, the process is monitored by a software agent known as the Supervisor Agent.
FAST has also the capability of implementing distributed FDI using its multi-agent architecture. The tool is able to partition complex industrial processes into subsystems, identify which process variables need to be shared by each subsystem and instantiate a Supervision Agent for each of the partitioned subsystems. The Supervision Agents once instantiated will start diagnosing their local components and handle the requests to provide the variable values which FAST has identified as shared with other agents to support the distributed FDI process.Per tal de facilitar la utilitzaciĂł d'eines per la detecciĂł i identificaciĂł de fallades (FDI) en la indĂșstria, es requereix un enfocament sistemĂ tic per permetre als enginyers de processos analitzar un sistema des d'aquesta perspectiva. D'aquesta forma, hauria de ser possible analitzar aquest sistema per determinar si proporciona el diagnosi de fallades i la redundĂ ncia d'acord amb la seva criticitat. A mĂ©s, hauria de ser possible avaluar escenaris de casos modificant lleugerament el procĂ©s (per exemple afegint sensors o canviant la seva localitzaciĂł) i avaluant l'impacte en quant a les possibilitats de diagnosi de fallades i redundĂ ncia. Per tant, aquest projecte proposa un enfocament per analitzar un procĂ©s des de la perspectiva FDI i per tal d'implementar-ho proporciona l'eina FAST la qual cobreix des de la fase d'anĂ lisi i disseny fins a la implementaciĂł final d'un supervisor FDI en un procĂ©s real. Per sintetitzar la informaciĂł del procĂ©s s'ha definit un format simple basat en XML. Aquest format proporciona la informaciĂł necessĂ ria per realitzar de forma sistemĂ tica l'AnĂ lisi Estructural del procĂ©s. Qualsevol procĂ©s pot ser analitzat, nomĂ©s hi ha la restricciĂł de que els models dels components han d'estar disponibles en l'eina FAST. Els processos es descriuen en termes de variables de procĂ©s, components i relacions i l'eina realitza l'anĂ lisi estructural obtenint: (i) la matriu estructural, (ii) el Perfect Matching, (iii) les relacions de redundĂ ncia analĂtica, si n'hi ha, i (iv) la matriu signatura de fallades. Per ajudar durant el procĂ©s d'anĂ lisi, FAST pot operar aĂŻlladament en mode de simulaciĂł permetent a l'enginyer de procĂ©s avaluar fallades, la seva detectabilitat i implementar canvis en els components del procĂ©s i la topologia per tal de millorar les capacitats de diagnosi i redundĂ ncia. Per altra banda, FAST pot operar en lĂnia connectat al procĂ©s de la planta per mitjĂ d'una interfĂcie OPC. La interfĂcie OPC permet la possibilitat de connectar gairebĂ© a qualsevol procĂ©s que inclogui un sistema SCADA per la seva supervisiĂł. Quan funciona en mode en lĂnia, el procĂ©s estĂ monitoritzat per un agent software anomenat l'Agent Supervisor. Addicionalment, FAST tĂ© la capacitat d'implementar FDI de forma distribuĂŻda utilitzant la seva arquitectura multi-agent. L'eina permet dividir sistemes industrials complexes en subsistemes, identificar quines variables de procĂ©s han de ser compartides per cada subsistema i generar una instĂ ncia d'Agent Supervisor per cadascun dels subsistemes identificats. Els Agents Supervisor un cop activats, començaran diagnosticant els components locals i despatxant les peticions de valors per les variables que FAST ha identificat com compartides amb altres agents, per tal d'implementar el procĂ©s FDI de forma distribuĂŻda.Postprint (published version
Assessing and augmenting SCADA cyber security: a survey of techniques
SCADA systems monitor and control critical infrastructures of national importance such as power generation and distribution, water supply, transportation networks, and manufacturing facilities. The pervasiveness, miniaturisations and declining costs of internet connectivity have transformed these systems from strictly isolated to highly interconnected networks. The connectivity provides immense benefits such as reliability, scalability and remote connectivity, but at the same time exposes an otherwise isolated and secure system, to global cyber security threats. This inevitable transformation to highly connected systems thus necessitates effective security safeguards to be in place as any compromise or downtime of SCADA systems can have severe economic, safety and security ramifications. One way to ensure vital asset protection is to adopt a viewpoint similar to an attacker to determine weaknesses and loopholes in defences. Such mind sets help to identify and fix potential breaches before their exploitation. This paper surveys tools and techniques to uncover SCADA system vulnerabilities. A comprehensive review of the selected approaches is provided along with their applicability
Evaluation of Containerized Simulation Software in Docker Swarm and Kubernetes
The modern industrial systems are large and complex so that a new simulation method, cooperative simulation or co-simulation, is used to simulate sub-models of a whole system. A large and complex system will be divided into several smaller subsystems, and these smaller systems will be modeled and simulated by multiple cooperative simulators. This simulation method enables the simulation process to be efficient and can provide many advantages, such as viewing results in real-time, consuming resources efficiently, and providing more accurate results than simulating the whole large and complex system.
Besides the co-simulation method, this thesis also introduces the Docker container technology, a container virtualization tool used to build and pull images, run containers, and orchestrate containers. Another container orchestrating tool, Kubernetes, is also used in the experiment for managing pods and containers. This thesis discusses the possibility of containerizing simulation software in Docker and uses Docker swarm and Kubernetes to orchestrate cooperative simulation containers. A co-simulation platform is created in a Docker swarm cluster and Kubernetes cluster, where multiple simulation containers are running cooperatively by receiving commands from the co-simulation platform. The experiment results prove that the co-simulation platform is working as expected, and that multiple cooperative simulation containers have better performance than running a standalone complex simulation process
Polynomial-Chaos-based Kriging
Computer simulation has become the standard tool in many engineering fields
for designing and optimizing systems, as well as for assessing their
reliability. To cope with demanding analysis such as optimization and
reliability, surrogate models (a.k.a meta-models) have been increasingly
investigated in the last decade. Polynomial Chaos Expansions (PCE) and Kriging
are two popular non-intrusive meta-modelling techniques. PCE surrogates the
computational model with a series of orthonormal polynomials in the input
variables where polynomials are chosen in coherency with the probability
distributions of those input variables. On the other hand, Kriging assumes that
the computer model behaves as a realization of a Gaussian random process whose
parameters are estimated from the available computer runs, i.e. input vectors
and response values. These two techniques have been developed more or less in
parallel so far with little interaction between the researchers in the two
fields. In this paper, PC-Kriging is derived as a new non-intrusive
meta-modeling approach combining PCE and Kriging. A sparse set of orthonormal
polynomials (PCE) approximates the global behavior of the computational model
whereas Kriging manages the local variability of the model output. An adaptive
algorithm similar to the least angle regression algorithm determines the
optimal sparse set of polynomials. PC-Kriging is validated on various benchmark
analytical functions which are easy to sample for reference results. From the
numerical investigations it is concluded that PC-Kriging performs better than
or at least as good as the two distinct meta-modeling techniques. A larger gain
in accuracy is obtained when the experimental design has a limited size, which
is an asset when dealing with demanding computational models
Honeywell Experion System: Configuration, simulation and process control software interoperability
The use of automated distributed control systems has become a widely discussed topic as industry attempts to maximise efficiency. An increase in automation technology has resulted in demand for âindustry readyâ graduate engineers with knowledge and experience with such technologies. Murdoch University (Murdoch) provides students with exposure to industry standard automation systems such as Honeywellâs Experion Process Knowledge System (PKS), a Distributed Control System (DCS) designed for the control and optimisation of industrial plants.
Presently, Murdoch uses Experion to monitor and control its pilot plant, exposing instrumentation and control students to the configuration and control of an industrial plant. Additionally, Murdoch holds a second Experion simulation license providing the ability to program and simulate process and control strategies. This project scope was to explore several key areas of the Experion simulation system followed by the development of a series of learning materials to facilitate the teaching of the Experion system to students for a new unit to be introduced at Murdoch University in 2017.
The methodology adopted to achieve the project outcome involved developing a comprehensive understanding of Experion and its associated applications, before building a series of example simulation programs for the purpose of implementing and testing a variety of Experionâs control strategies. Additionally, real-time control of the Experion simulation programs, using third party process control software, was achieved. The successful interoperability of MATLAB, Simulink, and LabVIEW with Experion provides an avenue for implementing advanced control strategies both in simulation and on Murdochâs pilot plant.
At the conclusion of the project, an extensive list of learning materials was produced, providing comprehensive procedures to enact tasks within the Experion system. This includes initial setup and configuration, development of simulated programs and associated Human Machine Interface (HMI) displays, the implementation of control strategies, and third party process control software interoperability. These learning materials provide students with an enhanced learning experience, giving them the skills and exposure required to thrive in the automation engineering industry as a Murdoch University graduate
PID control system analysis, design, and technology
Designing and tuning a proportional-integral-derivative
(PID) controller appears to be conceptually intuitive, but can
be hard in practice, if multiple (and often conflicting) objectives
such as short transient and high stability are to be achieved.
Usually, initial designs obtained by all means need to be adjusted
repeatedly through computer simulations until the closed-loop
system performs or compromises as desired. This stimulates
the development of "intelligent" tools that can assist engineers
to achieve the best overall PID control for the entire operating
envelope. This development has further led to the incorporation
of some advanced tuning algorithms into PID hardware modules.
Corresponding to these developments, this paper presents a
modern overview of functionalities and tuning methods in patents,
software packages and commercial hardware modules. It is seen
that many PID variants have been developed in order to improve
transient performance, but standardising and modularising PID
control are desired, although challenging. The inclusion of system
identification and "intelligent" techniques in software based PID
systems helps automate the entire design and tuning process to
a useful degree. This should also assist future development of
"plug-and-play" PID controllers that are widely applicable and
can be set up easily and operate optimally for enhanced productivity,
improved quality and reduced maintenance requirements
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