基于数据驱动的KPI系统最优滤波器设计

Data-driven based optimal filter design for KPI system

为了优化基于数据驱动的嵌入式故障诊断滤波器设计,采用KPI(key performance index)思想设计FDI(fault detection and isolation)残差,研究基于m DOs观测器的闭环Kalman滤波器设计方法,实现故障诊断和系统状态的有效观测.首先,基于采样数据得到大型复杂系统的KPI子空间模型,定义了跟踪误差,得到了闭环滤波器;其次,将残差序列表示为Hankel模型,通过定义正交投影补矩阵并选择恰当的数据列,构建出新的阈值矩阵;最后,得到Kalman滤波增益的计算方法,并给出了最优Kalman滤波器的设计步骤.结果表明,优化后残差的幅值降低至优化前的1/2.基于数据驱动的KPI系统最优滤波器设计,可提高对弱小故障监测的灵敏度,实现系统状态估计和故障诊断的性能优化.

In order to optimize the data-driven design of embedded fault diagnosis filters,a FDI（ fault detection and isolation） residual generator is presented based on KPI（ key performance index）,and a design method for the closed-loop Kalman filter based on the multi-diagnostic observers（ m DOs） is studied to realize fault diagnosis and effective observation for system status. First,the KPI subspace model for the large complex system is obtained based on the sampling data. The tracking error is defined,and the closed-loop filter is realized. Then,the residual sequence is expressed as the Hankel mode. A newthreshold matrix is constructed by defining an orthogonal projection complement matrix as well as selecting appropriate data columns. Finally,the calculation method for the Kalman filter gain is obtained,and the design steps for the optimal Kalman filter are described.The results showthat the amplitude of the optimized residual is half that of the pre-optimized residual. The data-driven based optimal filter design for the KPI system can improve the monitoring sensitivity for tiny fault and optimize both status estimating and fault diagnosis.