31,017 research outputs found
An integration of case-based and model-based reasoning and its application to physical system faults
Case-Based Reasoning (CBR) systems solve new problems by finding stored instances of problems similar to the current one, and by adapting previous solutions to fit the current problem, taking into consideration any differences between the current and previous situations. CBR has been proposed as a more robust and plausible model of expert reasoning than the better-known rule-based systems.;Current CBR systems have been used in planning, engineering design, and memory organization. There has been minimal work, however, in the area of reasoning about physical systems. This type of reasoning is a difficult task, and every attempt to automate the process must overcome the problems of modeling normal behavior, diagnosing faults, and predicting future behavior.;CBR systems are currently quite difficult to compare and evaluate, because there is currently no common mathematical framework in which the systems can be described. The only avenue available at present for comparison and evaluation of CBR systems requires an intellectual synthesis of the semantics of the program sources. Important constraints on the operation of a CBR system are often hidden in obscure programming tricks in the system\u27s source code.;This thesis presents a hybrid methodology for reasoning about physical systems in operation. This methodology is based on retrieval and adaptation of previously experienced problems similar to the problem at hand. In this methodology the ability of a CBR to reason about a physical system is significantly enhanced by the addition to the Case-Based Reasoner of a model of the physical system. The model describes the physical system\u27s structural, functional, and causal behavior.;Additionally, this thesis presents a mathematical formalization of the case-based reasoning paradigm and a formal specification of the interaction of the CBR component with the model-based component of a case-based system. to prove the feasibility and the merit of such methodology, a prototypical system for dealing with the faults of a physical system has been designed and implemented. Through testing has been proved that this hybrid methodology allows the generation of diagnoses and prognoses that are beyond the capabilities of current reasoning systems
Intelligent systems in manufacturing: current developments and future prospects
Global competition and rapidly changing customer requirements are demanding increasing changes in manufacturing environments. Enterprises are required to constantly redesign their products and continuously reconfigure their manufacturing systems. Traditional approaches to manufacturing systems do not fully satisfy this new situation. Many authors have proposed that artificial intelligence will bring the flexibility and efficiency needed by manufacturing systems. This paper is a review of artificial intelligence techniques used in manufacturing systems. The paper first defines the components of a simplified intelligent manufacturing systems (IMS), the different Artificial Intelligence (AI) techniques to be considered and then shows how these AI techniques are used for the components of IMS
Context constraint integration and validation in dynamic web service compositions
System architectures that cross organisational boundaries are usually implemented based on Web service technologies due to their inherent interoperability benets. With increasing exibility requirements, such as on-demand service provision, a dynamic approach to service architecture focussing on composition at runtime is needed. The possibility of technical faults, but also violations of functional and semantic constraints require a comprehensive notion of context that captures composition-relevant aspects. Context-aware techniques are consequently required to support constraint validation for dynamic service composition. We present techniques to respond to problems occurring during the execution of dynamically composed Web
services implemented in WS-BPEL. A notion of context { covering physical and contractual
faults and violations { is used to safeguard composed service executions dynamically. Our aim is to present an architectural framework from an application-oriented perspective, addressing practical considerations of a technical framework
Cyber-Virtual Systems: Simulation, Validation & Visualization
We describe our ongoing work and view on simulation, validation and
visualization of cyber-physical systems in industrial automation during
development, operation and maintenance. System models may represent an existing
physical part - for example an existing robot installation - and a software
simulated part - for example a possible future extension. We call such systems
cyber-virtual systems.
In this paper, we present the existing VITELab infrastructure for
visualization tasks in industrial automation. The new methodology for
simulation and validation motivated in this paper integrates this
infrastructure. We are targeting scenarios, where industrial sites which may be
in remote locations are modeled and visualized from different sites anywhere in
the world.
Complementing the visualization work, here, we are also concentrating on
software modeling challenges related to cyber-virtual systems and simulation,
testing, validation and verification techniques for them. Software models of
industrial sites require behavioural models of the components of the industrial
sites such as models for tools, robots, workpieces and other machinery as well
as communication and sensor facilities. Furthermore, collaboration between
sites is an important goal of our work.Comment: Preprint, 9th International Conference on Evaluation of Novel
Approaches to Software Engineering (ENASE 2014
Machine learning and its applications in reliability analysis systems
In this thesis, we are interested in exploring some aspects of Machine Learning (ML) and its application in the Reliability Analysis systems (RAs). We begin by investigating some ML paradigms and their- techniques, go on to discuss the possible applications of ML in improving RAs performance, and lastly give guidelines of the architecture of learning RAs. Our survey of ML covers both levels of Neural Network learning and Symbolic learning. In symbolic process learning, five types of learning and their applications are discussed: rote learning, learning from instruction, learning from analogy, learning from examples, and learning from observation and discovery. The Reliability Analysis systems (RAs) presented in this thesis are mainly designed for maintaining plant safety supported by two functions: risk analysis function, i.e., failure mode effect analysis (FMEA) ; and diagnosis function, i.e., real-time fault location (RTFL). Three approaches have been discussed in creating the RAs. According to the result of our survey, we suggest currently the best design of RAs is to embed model-based RAs, i.e., MORA (as software) in a neural network based computer system (as hardware). However, there are still some improvement which can be made through the applications of Machine Learning. By implanting the 'learning element', the MORA will become learning MORA (La MORA) system, a learning Reliability Analysis system with the power of automatic knowledge acquisition and inconsistency checking, and more. To conclude our thesis, we propose an architecture of La MORA
Use of COTS functional analysis software as an IVHM design tool for detection and isolation of UAV fuel system faults
This paper presents a new approach to the development of health management solutions which can be applied to both new and legacy platforms during the conceptual design phase. The approach involves the qualitative functional modelling of a system in order to perform an Integrated Vehicle Health Management (IVHM) design – the placement of sensors and the diagnostic rules to be used in interrogating their output. The qualitative functional analysis was chosen as a route for early assessment of failures in complex systems. Functional models of system components are required for capturing the available system knowledge used during various stages of system and IVHM design. MADe™ (Maintenance Aware Design environment), a COTS software tool developed by PHM Technology, was used for the health management design. A model has been built incorporating the failure diagrams of five failure modes for five different components of a UAV fuel system. Thus an inherent health management solution for the system and the optimised sensor set solution have been defined. The automatically generated sensor set solution also contains a diagnostic rule set, which was validated on the fuel rig for different operation modes taking into account the predicted fault detection/isolation and ambiguity group coefficients. It was concluded that when using functional modelling, the IVHM design and the actual system design cannot be done in isolation. The functional approach requires permanent input from the system designer and reliability engineers in order to construct a functional model that will qualitatively represent the real system. In other words, the physical insight should not be isolated from the failure phenomena and the diagnostic analysis tools should be able to adequately capture the experience bases. This approach has been verified on a laboratory bench top test rig which can simulate a range of possible fuel system faults. The rig is fully instrumented in order to allow benchmarking of various sensing solution for fault detection/isolation that were identified using functional analysis
CBR and MBR techniques: review for an application in the emergencies domain
The purpose of this document is to provide an in-depth analysis of current reasoning engine practice and the integration strategies of Case Based Reasoning and Model Based Reasoning that will be used in the design and development of the RIMSAT system.
RIMSAT (Remote Intelligent Management Support and Training) is a European Commission funded project designed to:
a.. Provide an innovative, 'intelligent', knowledge based solution aimed at improving the quality of critical decisions
b.. Enhance the competencies and responsiveness of individuals and organisations involved in highly complex, safety critical incidents - irrespective of their location.
In other words, RIMSAT aims to design and implement a decision support system that using Case Base Reasoning as well as Model Base Reasoning technology is applied in the management of emergency situations.
This document is part of a deliverable for RIMSAT project, and although it has been done in close contact with the requirements of the project, it provides an overview wide enough for providing a state of the art in integration strategies between CBR and MBR technologies.Postprint (published version
Space station advanced automation
In the development of a safe, productive and maintainable space station, Automation and Robotics (A and R) has been identified as an enabling technology which will allow efficient operation at a reasonable cost. The Space Station Freedom's (SSF) systems are very complex, and interdependent. The usage of Advanced Automation (AA) will help restructure, and integrate system status so that station and ground personnel can operate more efficiently. To use AA technology for the augmentation of system management functions requires a development model which consists of well defined phases of: evaluation, development, integration, and maintenance. The evaluation phase will consider system management functions against traditional solutions, implementation techniques and requirements; the end result of this phase should be a well developed concept along with a feasibility analysis. In the development phase the AA system will be developed in accordance with a traditional Life Cycle Model (LCM) modified for Knowledge Based System (KBS) applications. A way by which both knowledge bases and reasoning techniques can be reused to control costs is explained. During the integration phase the KBS software must be integrated with conventional software, and verified and validated. The Verification and Validation (V and V) techniques applicable to these KBS are based on the ideas of consistency, minimal competency, and graph theory. The maintenance phase will be aided by having well designed and documented KBS software
A fault-tolerant intelligent robotic control system
This paper describes the concept, design, and features of a fault-tolerant intelligent robotic control system being developed for space and commercial applications that require high dependability. The comprehensive strategy integrates system level hardware/software fault tolerance with task level handling of uncertainties and unexpected events for robotic control. The underlying architecture for system level fault tolerance is the distributed recovery block which protects against application software, system software, hardware, and network failures. Task level fault tolerance provisions are implemented in a knowledge-based system which utilizes advanced automation techniques such as rule-based and model-based reasoning to monitor, diagnose, and recover from unexpected events. The two level design provides tolerance of two or more faults occurring serially at any level of command, control, sensing, or actuation. The potential benefits of such a fault tolerant robotic control system include: (1) a minimized potential for damage to humans, the work site, and the robot itself; (2) continuous operation with a minimum of uncommanded motion in the presence of failures; and (3) more reliable autonomous operation providing increased efficiency in the execution of robotic tasks and decreased demand on human operators for controlling and monitoring the robotic servicing routines
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