Module level fault diagnosis for analog circuits based on system identification and genetic algorithm

This paper proposes a new module level fault diagnosis method for analog circuits. Firstly, the transfer function is constructed according to the relationship between output and input of the circuit under test (CUT). Every system parameter of the transfer function is expressed by several component parameters. These components are divided into several modules. Then, the way of objective function optimization based on genetic algorithm (GA) is adopted to solve nonlinear equations, which are obtained by multi-frequency testing. Finally, the module level faults are detected by comparing the estimated system parameters to their normal values. The results show that the proposed method is effective to identify system parameters and locate module level faults.

► The system identification tool is used to construct circuit transfer function.
► Genetic algorithm is adopted to search global optimal system parameters.
► The optimal test nodes are selected according to the division of the component module.
► In addition to effectively identify fault, the proposed method can judge the extent of damage to fault module.