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In industrial plants, the profitability of the plant is significantly affected by the quality of machines maintenance. To ensure continuous production, the high valued machines should be kept in good working conditions. This brings plants to search for means to control and reduce equipment failures. When faults emerge in plants, appropriate actions for fault diagnosis and troubleshooting must be executed promptly and effectively to prevent large costs due to breakdowns. To provide reliable and effective maintenance support, the aid of advanced decision support technology utilizing previous repair experience is of crucial importance for the expert operators as it provides them valuable troubleshooting clues for new faults. Artificial intelligence (AI) technology, particularly, knowledge-based approach is promising for this domain. It captures efficiency of problem solving expertise from the domain experts; guides the expert operators in rapid fault detection and troubleshooting. This paper focuses on the design and development of a Knowledge-Intensive Decision Support System (KI-DSS) for Maintenance, Repair and Service in industrial plants to support better maintenance decision and improve maintenance efficiency. With integration of case-based Reasoning and ontology, the Ki- DSS not only carries out data matching retrieval, but also performs semantic associated data access which is important for intelligent knowledge retrieval in decision support system. A case is executed to illustrate the use of the proposed KI-DSS to show the feasibility of our approach and the benefit of the ontology support. |
