Multiple Fault Diagnosis Based on Multiple Fault Simulation Using Particle Swarm Optimization

Abstract

Fault diagnosis is extremely important to ramp up the manufacturing yield and in some cases to reduce the product debug time as well. In this paper, we have proposed a novel technique to analyze multiple fault diagnosis based on multiple fault injection. Almost all the conventional fault diagnosis method simulate one fault (among the candidate faults) at a time and based on the number of failed patterns the fault can explain, a ranking is proposed for all candidate faults. But, a single fault injection cannot manifest the effect of multiple faults that are present in the actual failed circuit. Thus, in this paper, we have injected multiple faults simultaneously, and perform an effect-cause analysis to find the possible list of faults. Experimental results prove the validation of our approach as it has high diagnosability and resolution. The proposed method runs within moderate CPU time. We have been able to run simulations to diagnose upto 10 faults in a reasonable time. However, the scheme does not put any restrictions on the number of simultaneous faults.