Genetic Algorithm Based Faulty Parameter Identification for Linear Analog Circuit

Abstract

Faulty parameter identification is vital to prediction of remaining useful life of circuit under test (CUT). Based on the transfer function of the CUT, we use measured faulty response to reversely deduce the possible faulty parameters. It is known that the response is the function of the analog parameters. Usually, the number of independent response is much fewer than that of the analog component. The underdetermined equations have infinite solutions. In other words, there are lots of combinations of analog parameters that can generate the same faulty response. Hence, it is more valuable to deduce the possible fault parameter range within which all faults can generate the same measured response. The deduction is accomplished by using genetic algorithm. The length of chromosome equals to the number of analog component. Each gene represents a component's parameter value. Based on the gene values and the transfer function, each individual has a simulated response. Our target is to find all possible individuals viz., parameter combinations, which minimize the difference between the simulated and measured faulty response. In the final solutions, it is easy to find the minimal and maximum fault parameters, viz., extreme points. The possible faulty parameter rage is determined by these two extreme points because that the transfer function and parameters of analog element are continuous. The effectiveness of the proposed method is examined by using filter circuit examples.