Intelligent Predictive Decision Support System for Condition-Based Maintenance

• Published in: International journal of advanced manufacturing technology Vol. 17; no. 5; pp. 383 - 391
• Main Authors: Yam, R. C. M., Tse, P.W., Li, L., Tu, P.
• Format: Journal Article
• Language: English
• Published: Heidelberg Springer Nature B.V 01.02.2001
• Subjects: Catastrophic events; Decision support systems; Deterioration; Electric power generation; Fault diagnosis; Maintenance management; Management systems; Predictive maintenance; Recurrent neural networks; Repair & maintenance; Schedules; Spare parts
• ISSN: 0268-3768; 1433-3015
• DOI: 10.1007/s001700170173

The high costs in maintaining today’s complex and sophisticated equipment make it necessary to enhance modern maintenance management systems. Conventional condition-based maintenance (CBM) reduces the uncertainty of maintenance according to the needs indicated by the equipment condition. The intelligent predictive decision support system (IPDSS) for condition-based maintenance (CBM) supplements the conventional CBM approach by adding the capability of intelligent condition-based fault diagnosis and the power of predicting the trend of equipment deterioration. An IPDSS model, based on the recurrent neural network (RNN) approach, was developed and tested and run for the critical equipment of a power plant. The results showed that the IPDSS model provided reliable fault diagnosis and strong predictive power for the trend of equipment deterioration. These valuable results could be used as input to an integrated maintenance management system to pre-plan and pre-schedule maintenance work, to reduce inventory costs for spare parts, to cut down unplanned forced outage and to minimise the risk of catastrophic failure.