Computational Intelligence in Design and Manufacturing
Computational Intelligence in Design and Manufacturing
Rough Sets: Theoretical Aspects of Reasoning about Data
Rough Sets: Theoretical Aspects of Reasoning about Data
Artificial Intelligence in Chemical Engineering
Artificial Intelligence in Chemical Engineering
Rough Set Learning of Preferential Attitude in Multi-Criteria Decision Making
ISMIS '93 Proceedings of the 7th International Symposium on Methodologies for Intelligent Systems
HYDES: A Web-based hydro turbine fault diagnosis system
Expert Systems with Applications: An International Journal
Rule induction based on an incremental rough set
Expert Systems with Applications: An International Journal
A rough set based approach to patent development with the consideration of resource allocation
Expert Systems with Applications: An International Journal
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Fault diagnosis is a complex and difficult problem that concerns effective decision-making. Carrying out timely system diagnosis whenever a fault symptom is detected would help to reduce system down time and improve the overall productivity. Due to the knowledge and experience intensive nature of fault diagnosis, the diagnostic result very much depends on the preference of the decision makers on the hidden relations between possible faults and the presented symptom. In other words, fault diagnosis is to rank the possible faults accordingly to give the engineer a practical priority to carry out the maintenance work in an efficient and orderly manner. This paper presents a rough set-based prototype system that aims at ranking the possible faults for fault diagnosis. The novel approach engages rough theory as a knowledge extraction tool to work on the past diagnostic records, which is registered in a pair-wise comparison table. It attempts to extract a set of minimal diagnostic rules encoding the preference pattern of decision-making by domain experts. By means of the knowledge acquired, the ordering of possible faults for failure symptom can then be determined. The prototype system also incorporates a self-learning ability to accumulate the diagnostic knowledge. A case study is used to illustrate the functionality of the developed prototype. Result shows that the ranking outcome of the possible faults is reasonable and sensible.