Fault diagnosis of analog filter circuit based on genetic algorithm

Abstract

Hard (open and short) faults and discrete parameter faults (DPFs) are the mostly used fault models in the simulation-before-test (SBT) method. Because the parameter of the analog element is continuous, the DPF cannot elaborately characterize all possible continuous parameter faults (CPF) occurring in the analog circuit. To address such a problem, a genetic algorithm (GA)-based simulation after the test (SAT) fault diagnosis method is proposed in this paper. The fault diagnosis is transformed into an optimization problem. The genes represent the parameter values of potential faulty components. The faulty circuit response is the objective. Our target is to minimize the difference between the actual faulty response and the GA simulated response. The chromosome that minimizes the difference gives the solution. This method does not save all possible faults in advance while it can diagnosis all continuous fault values. The effectiveness of the proposed method is examined by using filter circuit examples.