15,269 research outputs found
COMBINED DEEP AND SHALLOW KNOWLEDGE IN A UNIFIED MODEL FOR DIAGNOSIS BY ABDUCTION
Fault Diagnosis in real systems usually involves human expert’s shallow knowledge (as pattern causes-effects) but also deep knowledge (as structural / functional modularization and models on behavior). The paper proposes a unified approach on diagnosis by abduction based on plausibility and relevance criteria multiple applied, in a connectionist implementation. Then, it focuses elicitation of deep knowledge on target conductive flow systems – most encountered in industry and not only, in the aim of fault diagnosis. Finally, the paper gives hints on design and building of diagnosis system by abduction, embedding deep and shallow knowledge (according to case) and performing hierarchical fault isolation, along with a case study on a hydraulic installation in a rolling mill plant.shallow knowledge, diagnosis, flow systems
Combined Deep and Shallow Knowledge in a Unified Model for Diagnosis by Abduction
Fault Diagnosis in real systems usually involves human expert’s shallow knowledge (as pattern causes-effects) but also deep knowledge (as structural / functional modularization and models on behavior). The paper proposes a unified approach on diagnosis by abduction based on plausibility and relevance criteria multiple applied, in a connectionist implementation. Then, it focuses elicitation of deep knowledge on target conductive flow systems – most encountered in industry and not only, in the aim of fault diagnosis. Finally, the paper gives hints on design and building of diagnosis system by abduction, embedding deep and shallow knowledge (according to case) and performing hierarchical fault isolation, along with a case study on a hydraulic installation in a rolling mill plant.Faulty Diagnosis, abduction, plausibility criteria, relevant criterion
Combined deep and shallow knowledge in a unified model for diagnosis by abduction
Fault Diagnosis in real systems usually involves human expert’s shallow knowledge (as pattern causes-effects) but also deep knowledge (as structural / functional modularization and models on behavior). The paper proposes a unified approach on diagnosis by abduction based on plausibility and relevance criteria multiple applied, in a connectionist implementation. Then, it focuses elicitation of deep knowledge on target conductive flow systems – most encountered in industry and not only, in the aim of fault diagnosis. Finally, the paper gives hints on design and building of diagnosis system by abduction, embedding deep and shallow knowledge (according to case) and performing hierarchical fault isolation, along with a case study on a hydraulic installation in a rolling mill plant
The Diagnosis by Abduction using Human Expert Knowledge
Fault Diagnosis in real systems usually involves human expert’s shallow knowledge (as pattern causes-effects) but also deep knowledge (as structural / functional modularization and models on behavior). The paper proposes a unified approach on diagnosis by abduction based on plausibility and relevance criteria multiple applied, in a connectionist implementation. Then, it focuses elicitation of deep knowledge on target conductive flow systems – most encountered in industry and not only, in the aim of fault diagnosis. Finally, the paper gives hints on design and building of diagnosis system by abduction, embedding deep and shallow knowledge (according to case) and performing hierarchical fault isolation, along with a case study on a hydraulic installation in a rolling mill plant
The Galileo PPS expert monitoring and diagnostic prototype
The Galileo PPS Expert Monitoring Module (EMM) is a prototype system implemented on the SUN workstation that will demonstrate a knowledge-based approach to monitoring and diagnosis for the Galileo spacecraft Power/Pyro subsystems. The prototype will simulate an analysis module functioning within the SFOC Engineering Analysis Subsystem Environment (EASE). This document describes the implementation of a prototype EMM for the Galileo spacecraft Power Pyro Subsystem. Section 2 of this document provides an overview of the issues in monitoring and diagnosis and comparison between traditional and knowledge-based solutions to this problem. Section 3 describes various tradeoffs which must be considered when designing a knowledge-based approach to monitoring and diagnosis, and section 4 discusses how these issues were resolved in constructing the prototype. Section 5 presents conclusions and recommendations for constructing a full-scale demonstration of the EMM. A Glossary provides definitions of terms used in this text
Knowledge-based diagnosis for aerospace systems
The need for automated diagnosis in aerospace systems and the approach of using knowledge-based systems are examined. Research issues in knowledge-based diagnosis which are important for aerospace applications are treated along with a review of recent relevant research developments in Artificial Intelligence. The design and operation of some existing knowledge-based diagnosis systems are described. The systems described and compared include the LES expert system for liquid oxygen loading at NASA Kennedy Space Center, the FAITH diagnosis system developed at the Jet Propulsion Laboratory, the PES procedural expert system developed at SRI International, the CSRL approach developed at Ohio State University, the StarPlan system developed by Ford Aerospace, the IDM integrated diagnostic model, and the DRAPhys diagnostic system developed at NASA Langley Research Center
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Knowledge-based approaches to fault diagnosis. The development, implementation, evaluation and comparison of knowledge-based systems, incorporating deep and shallow knowledge, to aid in the diagnosis of faults in complex hydro-mechanical devices.
The use of knowledge-based systems to aid in the diagnosis of faults in physical
devices has grown considerably since their introduction during the 1970s. The
majority of the early knowledge-based systems incorporated shallow knowledge,
which sought to define simple cause and effect relationships between a symptom and
a fault, that could be encoded as a set of rules. Though such systems enjoyed much
success, it was recognised that they suffered from a number of inherent limitations
such as inflexibility, inadequate explanation, and difficulties of knowledge elicitation.
Many of these limitations can be overcome by developing knowledge-based systems
which contain deeper knowledge about the device being diagnosed. Such systems,
now generally referred to as model-based systems, have shown much promise, but
there has been little evidence to suggest that they have successfully made the
transition from the research centre to the workplace.
This thesis argues that knowledge-based systems are an appropriate tool for the
diagnosis of faults in complex devices, and that both deep and shallow knowledge
have their part to play in this process. More specifically this thesis demonstrates how
a wide-ranging knowledge-based system for quality assurance, based upon shallow
knowledge, can be developed, and implemented. The resultant system, named
DIPLOMA, not only diagnoses faults, but additionally provides advice and guidance
on the assembly, disassembly, testing, inspection and repair of a highly complex
hydro-mechanical device. Additionally it is shown that a highly innovative modelbased
system, named MIDAS, can be used to contribute to the provision of
diagnostic, explanatory and training facilities for the same hydro-mechanical device.
The methods of designing, coding, implementing and evaluating both systems are
explored in detail.
The successful implementation and evaluation of the DIPLOMA and MIDAS
systems has shown that knowledge-based systems are an appropriate tool for the
diagnosis of faults in complex hydro-mechanical devices, and that they make a
beneficial contribution to the business performance of the host organisation.
Furthermore, it has been demonstrated that the most effective and comprehensive
knowledge-based approach to fault diagnosis is one which incorporates both deep and
shallow knowledge, so that the distinctive advantages of each can be realised in a
single application. Finally, the research has provided evidence that the model-based
approach to diagnosis is highly flexible, and may, therefore, be an appropriate
technique for a wide range of industrial applications.Science and Engineering Research Council, and Alvey Directorat
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
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