Abstract: The operation process of ironmaking equipment is varied and complex, so forecasting and analysis of equipment malfunction need to satisfy the modern requirements of equipment management. A malfunction prediction model of ironmaking equipment was established by the combination of gray system GM(1, 1) model and metabolism model. By analyzing the real-time data and finding the law among them, the model can predict the reliability of equipment more precisely. The monitoring of Anyang steel hot blast stove showed that this model can analyse the data timely according to the real time status of equipment. This model can work faster, more convenient and reliable than the traditional method, which can reduce significantly the simulated calculating works and improve the accuracy of prediction.