Full metadata record
| DC Field | Value | Language |
|---|---|---|
| dc.contributor | Multi-disciplinary Studies | en_US |
| dc.contributor | Department of Electronic and Information Engineering | en_US |
| dc.creator | Lau, Kong-lui | - |
| dc.identifier.uri | https://theses.lib.polyu.edu.hk/handle/200/466 | - |
| dc.language | English | en_US |
| dc.publisher | Hong Kong Polytechnic University | - |
| dc.rights | All rights reserved | en_US |
| dc.title | Fuzzy techniques for modern train equipment maintenance | en_US |
| dcterms.abstract | A human-made system is often created within a bigger system in order to bring improvements to this bigger system. Nowadays computer technology is pervasively applied in human-made systems. Human involved in the use and operation of these systems interact a lot with the computers and computer-based information. With fuzzy set theory and fuzzy logic, we no longer need to believe that human's thinking would be digitized so as to suit the computer technology. Instead, we can preserve the fuzziness inherent in human's knowledge, and design the computer technology to understand and handle this fuzziness in human-made systems. This dissertation looks into the application of fuzzy logic techniques for modern train equipment maintenance. The aim is to explore the modelling of the fuzziness in the fault-finding knowledge possessed by the maintenance staff, and to study the development of expert system for fault diagnosis. In this dissertation, component failure mode is viewed as an abstraction of component misbehaviour, which gives rise to system fault symptom. The inference mechanism is based on fuzzy inference principles, in which the degree of truth of an explanatory failure mode is given by the degree of fulfillment of the fuzzy rule. The degree of matching between the antecedents of the fuzzy rule and input fuzzy variables determines the degree of fulfillment of the fuzzy rule. The design of fuzzy system for fault diagnosis is then discussed in this dissertation. Finally, the fault finding reasoning method proposed in this dissertation is tested by using the FuzzyCLIPS expert system development tool. | en_US |
| dcterms.extent | 78 leaves : ill. ; 30 cm | en_US |
| dcterms.isPartOf | PolyU Electronic Theses | en_US |
| dcterms.issued | 1999 | en_US |
| dcterms.educationalLevel | All Master | en_US |
| dcterms.educationalLevel | M.Sc. | en_US |
| dcterms.LCSH | Railroads -- Trains -- Maintenance and repair -- Data processing | en_US |
| dcterms.LCSH | Expert systems (Computer science) | en_US |
| dcterms.LCSH | Fuzzy systems | en_US |
| dcterms.LCSH | Fault location (Engineering) | en_US |
| dcterms.LCSH | Hong Kong Polytechnic University -- Dissertations | en_US |
| dcterms.accessRights | restricted access | en_US |
Files in This Item:
| File | Description | Size | Format | |
|---|---|---|---|---|
| b14925631.pdf | For All Users (off-campus access for PolyU Staff & Students only) | 3.11 MB | Adobe PDF | View/Open |
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