强跟踪平方根容积卡尔曼滤波和 自回归模型融合的故障预测

Fault prediction with combination of strong tracking square-root cubature Kalman filter and autoregressive model

中文关键词  强跟踪滤波  非线性滤波  状态和参数联合估计  平方根容积卡尔曼滤波(SCKF)  故障预测

英文关键词  strong tracking filter  nonlinear filters  state and parameter joint estimation  square-root cubature Kalman filter (SCKF)  fault prediction

中文摘要

      为了解决非线性系统中不可测量参数的预测问题, 提出一种带有次优渐消因子的强跟踪平方根容积卡尔 曼滤波(STSCKF)和自回归(AR)模型相结合的故障预测方法. 利用AR模型时间序列预测法预测未来时刻的测量值, 将预测的测量值作为STSCKF的测量变量, 从而将预测问题转化为滤波估计问题. STSCKF通过在预测误差方差阵 的均方根中引入渐消因子调节滤波过程中的增益矩阵, 克服了故障参数变化函数未知情况下普通SCKF跟踪故障参 数缓慢甚至失效的局限性, 使得STSCKF能较好地预测故障参数的发展趋势. 连续搅拌反应釜(CSTR)仿真结果表 明, STSCKF的预测精度高于普通SCKF和强跟踪无迹卡尔曼滤波(STUKF), 验证了方法的有效性.

英文摘要

      To deal with the problem of prognosis of unmeasured parameters in nonlinear systems, we propose a fault prediction algorithm which is a combination of the strong tracking square-root cubature Kalman filter (STSCKF) with suboptimal fading factor and the autoregressive (AR) model. Future time values of measurement variables are forecasted by using the AR model time series prediction method; and then, the predicted values are used as STSCKF measurement variables. Thus, the prognostic problem is transformed into a filter estimation issue. The fading factor is introduced into the square root of the STSCKF prediction error covariance for adjusting the gain matrix in the filter process. As a result, STSCKF eliminates the disadvantage of slow tracking or even unable tracking of fault parameters in conventional SCKF when the time-varying functions of fault parameters are unknown, improving the capability for forecasting the varying trend of fault parameters. Simulation results on a continuous stirred tank reactor (CSTR) show that the predicting accuracy of STSCKF is higher than that of the conventional SCKF or the strong tracking unscented Kalman filter (STUKF), demonstrating the superiority of the performance capability of the proposed method.