基于比例差分型迭代学习的故障诊断算法

Fault diagnosis algorithm based on proportional difference type iteration learning

因为微分运算会给系统带来不良影响,所以为了避免在迭代学习算法中使用微分运算,同时又可以取得比单纯比例型迭代学习算法较快的收敛速度,将比例差分型迭代学习策略应用到故障诊断中,提出了一种新的故障诊断算法。该算法利用残差以及相邻两次残差的差分信号对引入的虚拟故障信号进行逐次修正,使虚拟故障逼近系统中实际发生的故障,从而达到对系统故障诊断的目的,并通过压缩映射方法,对故障跟踪估计器的收敛性进行了严格证明。该方法不仅可以有效地检测出系统不同类型的故障,还可以精确估计出各种故障信号。最后仿真结果验证了该方法的有效性。

To avoid the bad effects on differential calculation and to possess a faster convergent speed rela- tive to simple-proportional-type algorithm in the process of the iterative learning, a new fault diagnosis algo- rithm is proposed by applying the scheme of proportional difference type learning to fault diagnosis. This algo- rithm uses residual errors and the differential signal of the adjacent two residual errors to correct the virtual fault signals, which enables the virtual fault to appromixiate the fault occurred actually in the system, thereby attai- ning the end of system fault diagnosis. The convergence of the fault estimator is proven by the contraction map- ping approach. This algorithm can not only detect different-type fault of the system effectively, but also esti- mate the fault signal accurately. Finally, simulation results verify the validity of the algorithm.