CAJ | 학술논문

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。
작위련강엄적관건설비，풍궤담부착전로제진화매기회수적중요임무，실현풍궤잉여사용수명적준학예측구유중요적실제의의。통과대함단모련강엄풍궤진동수거적분석，건립료기우 Wiener 과정적상태퇴화모형，재수체시간적의의하，추도출풍궤잉여사용수명적개솔밀도함수적해석표체식，제출료일충기우겁대사연고계적삼수실시고계방법，종이실현풍궤잉여사용수명적재선실시예측。실험결과표명，상대우문헌중적방법，본문소제출적예측방법가이득도경고적예측정도화교저적예측불학정성。
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.