The effectivity analysis of adding sensors for improving model based fault isolability properties

Using structural model decomposition, the equations and variables of a system model are separated into different categories. Then, the association relations between the fault equations can be extracted, which imply the fault isolability. According to the partitions of variables, the selection space of adding sensors is significantly reduced. It is useful to optimize sensor placement and enhance fault isolability. Moreover, our approach need not build a fault signature matrix for fault diagnosis. The efficient algorithms based on this approach are proposed and the simulation of a four-tank system indicates the validity of the method.