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中文摘要：
作为炼钢厂的关键设备，风机担负着转炉除尘和煤气回收的重要任务，实现风机剩余使用寿命的准确预测具有重要的实际意义。通过对邯郸某炼钢厂风机振动数据的分析，建立了基于 Wiener 过程的状态退化模型，在首达时间的意义下，推导出风机剩余使用寿命的概率密度函数的解析表达式，提出了一种基于极大似然估计的参数实时估计方法，从而实现风机剩余使用寿命的在线实时预测。实验结果表明，相对于文献中的方法，本文所提出的预测方法可以得到更高的预测精度和较低的预测不确定性。
英文摘要：
As a crucial device of steel mills, the draught fan plays a key role in converter dedusting and gas recycling, and thus it is significantly essential to predict the remaining useful life （RUL） of the draught fan. In this paper, a Wiener process-based degradation model is constructed based on vibration data analysis for a draught fan in the Handan steel mill. An analytical expression of the probability density function （PDF） of RUL is derived on the concept of the first hitting time （FHT）. A parameter updating scheme is deduced on the basis of the maximum likelihood estimation （MLE） algorithm for the RUL online prediction of the draught fan. Comparative studies with existing models show that the proposed method can predict the RUL of the draught fan in real time with a higher accuracy and less uncertainties.
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