A fault prediction method that uses improved case-based reasoning to continuously predict the status of a shaft furnace

For the problem of predicting faults in the status of a shaft furnace, the missed alarm rate and false alarm rate have not been improved significantly by the traditional case-based reasoning (CBR) method. To predict faults more accurately, an improved CBR-based fault prediction method (ICBRP) is proposed in this paper. This ICBRP is composed of a water-filling theory-based weight allocation (WFA) model and a group decision-making-based revision (GDMR) model. According to the optimal allocation mechanism of channel power, a Lagrange function is designed to calculate the weights. Moreover, the credibility of historical results is used to revise the predicted results via the definition of a group utility function. Then, the proposed reasoning strategy can obtain more reasonable weights and take full advantage of comprehensive information from the retrieval results. Finally, the application results indicate that the proposed method is superior to traditional CBR and other methods. This proposed ICBRP significantly reduces the missed alarm rate and the false alarm rate of failure in the furnace status.