Performance Modeling Of Automated Systems

The text is designed for engineering students at the senior undergraduate level and first-year students at graduate level, and professionals (R&D engineers in the industry and factory managers). The authors offer a unique effort in presenting a unified and systematic treatment of various modeling methodologies and analysis techniques for performance evaluation of automated manufacturing systems. The text begins with an overview of automated manufacturing systems, and then provides a clear and comprehensive discussion of three principal analytical modeling paradigms: Markov Chains, Queues and Queuing Networks, and Petri Nets. Salient Features • Present the first ever treatment of the mathematical modeling of manufacturing systems. • Offers a unified study of principal analytical modeling paradigms for automated manufacturing systems. • Discusses many recent research contributions in the area of modeling of automated manufacturing systems. • Discusses many recent research contributions in the area of modeling of automated manufacturing systems, including deadlock modeling, transient analysis, queuing network approximations, Petri Net modeling, and integrated analytical modeling. • Provides a large number of exercises and problems.

This book describes methods to improve software performance and safety using advanced mathematical and computational analytics. The main focus is laid on the increase of software reliability by preventive and predictive maintenance with efficient usage of modern testing resources. The editors collect contributions from international researchers in the field.

Control and Dynamic Systems: Advances in Theory and Applications, Volume 47: Manufacturing and Automation Systems: Techniques and Technologies, Part 3 of 5 deals with techniques and technologies in manufacturing and automation systems. This book discusses techniques in modeling and control policies for production networks; effective planning and control of day-to-day operations; evaluation of automated manufacturing systems; the use of Petri Nets in modeling, control and performance analysis of automated manufacturing systems; and concurrent engineering and evaluation of concurrency in engineering design. The final chapter discusses the algorithm for solving allocation problems. This book will provide a uniquely significant reference source for practitioners in the field who want a comprehensive source of techniques with significant applied implications.

Designing and scaling complex, networked automation systems is a challenging task. Developers must consider different kinds of influence factors that impact the system performance already in the early stages of the development. If performance bottlenecks are detected too late, costly corrections may follow and the commissioning can be significantly delayed.There already exist several approaches to predict the performance of a system under development. However, developers must identify and decide themselves which factors must be regarded for a performance analysis and to what detail. Therefore, developers usually require in-depth knowledge of the system which is -- at least to this level of detail -- not available until the discipline specific development starts. In particular it is not clear, which factors influence the performance of a PLC and how these can be used for an early validation of a system model.The first contribution of this thesis is the identification and creation of a list of influence factors that impact one or more quality of service attributes of an automation system. For each factor, a decision is made, whether it is available in the early development stages, what assumptions must be made by the developer, and what impact it has on the overall system performance.The second contribution is a method that enables developers of automated systems to define the identified influence factors in systems engineering models and to carry out a subsequent automatic performance analysis. The method includes a model for the specification of influence factors within an existing Systems Engineering model as well as an accompanying process. Based on these models, an analysis can be performed to predict the utilization of a selected PLC. The results can be used to evaluate the design of the automation in the early phases and, therefore, avoid costly and ... ; eng

Based on the six-year NASA Aviation Safety and Security Program Human Performance Modeling project, a collaboration of five teams from industry and academia, Human Performance Modeling in Aviation chronicles the results of modeling NASA-supplied data on two aviation flight deck problems: pilot surface operations taxi errors, and approach and landing with synthetic vision systems. The book provides a deep understanding of the aviation problems and “what-if” system redesigns of flight deck technologies and procedures. Five modeling teams describe how they applied their models to these two problems and discuss the results in terms of the specific problems addressed, the modeling challenges faced, and the modeling solutions developed to address complex, real-world situations. The book then compares the five modeling tools used, shedding light on the unique approach that each brings to bear on two qualitatively different problems. It includes a “virtual roundtable discussion” that poses questions to each of the five teams and offers take-home lessons and insights into the modeling process and its complexities. The modeling teams also explore the issue of model validation and the approach that they adopted. Concluding with a summary of how modeling fits into the system design and evaluation process, the text covers state-of-the-art advances in human performance modeling for complex systems. Critical for modeling aviation-domain tasks, these modeling capabilities can also be applied to other complex-system domains such as process control, medical applications, surface transportation, and military command and control, which share similar human-system interaction issues.

In recent years, there has been growing interest in industrial systems, especially in robotic manipulators and mobile robot systems. As the cost of robots goes down and become more compact, the number of industrial applications of robotic systems increases. Moreover, there is need to design industrial systems with intelligence, autonomous decision making capabilities, and self-diagnosing properties. Intelligent Industrial Systems: Modeling, Automation and Adaptive Behavior analyzes current trends in industrial systems design, such as intelligent, industrial, and mobile robotics, complex electromechanical systems, fault diagnosis and avoidance of critical conditions, optimization, and adaptive behavior. This book discusses examples from major areas of research for engineers and researchers, providing an extensive background on robotics and industrial systems with intelligence, autonomy, and adaptive behavior giving emphasis to industrial systems design.