Research On Operation And Maintenance Decision Support System For Power Plant Equipment

With the rapid development of electric power, the problem of maintenance deficiency and surplus emerges in the schedule maintenance mode, which is adopted for power plant equipment in the past. So, it is turning into advanced condition base maintenance mode. The successful implementing of condition based maintenance for power plant equipment needs its support systems. Now, the researches of the support systems are focused on monitoring and diagnosis system (MDS) and computerized maintenance management system (CMMS). It is of great significance to research on operation and maintenance decision support system, which connects MDS with CMMS using useful information in power plant.In reliability-centered maintenance (RCM), maintenance strategies are made not only through analyzing failure mode but also the relationship of performance, economic and maintenance strategy. RCM becomes a more and more popular maintenance analysis method for complex system. But, in traditional RCM, the efficiency and accuracy of maintenance analysis are degraded for the absence of quantificational tools for equipment importance analysis, condition evaluation and maintenance optimization etc. Based on such a consideration, a streamlined RCM analysis method is put forward and the decision process of operation and maintenance for equipment in power plant is determined. Then some key technologies of equipment important analysis, condition evaluation and prediction, maintenance decision and optimization are studied.Aiming at the imprecision of existing equipment important analysis methods resulted from using too many subjective factors, the pivotal factors effecting equipment importance are analyzed, and an importance analyzing method based on Monte Carlo simulation is provided. Then, equipment is classified according to importance criterion, and the rules of maintenance mode decision are established. For complex equipment, a quantificational failure criticality analysis model based on failure mode and effect analysis (FMEA) and grey theory is put forward. The precision of criticality analysis is improved, and can be a support for characteristic parameters extraction in condition evaluation.A multi-parameters synthetic condition evaluation model based on variable weight and fuzzy theory is used. In the model, the on-line and off-line monitoring and diagnosis data, operation real-time data, reliability analysis data, life assessment data and the history data of operation and maintenance are fully used, the evaluation result is closer to the real condition. A synthetic condition prediction model is presented, using neural network and grey theory together make it possible to predict accurately. For the system with many characteristic parameters, a neural network condition prediction model based on principle component analysis (PCA) is studied. Prediction speed and precision are improved by decreasing the dimensions of neural network input.For maintenance decision and optimization, some maintenance task decision and optimization models using maintenance history data and results of condition evaluation and prediction are introduced, and their solutions provided. A short-term maintenance decision method is given for complex repairable system. By fully considering technique...