Diagnosis of multiple arbitrary faults with mask and reinforcement effect
Jing Ye, Yu Hen Hu, Xiaowei Li · 2010
Abstract—We propose a multiple-fault diagnosis method with high diagnosability, resolution, first-hit and short run time. The method has no assumption on fault models, thus can diagnose arbitrary faults. To cope with the multiple-fault mask and reinforcement effect, two key techniques of construction and scoring of fault-tuple equivalence trees are introduced to choose and rank the final candidate locations. Experimental results show that, when the circuits have 2 arbitrary faults, the average diagnosability and resolution are 98 % and 0.95, respectively, with the best case 100 % and 1.00. Moreover, in average, even when 21 arbitrary faults exist, our method can still identify 93% of them with the resolution 0.78, increased by 41 % and 39 % in comparison with the latest work where the diagnosability and resolution are 66 % and 0.56. Finally, 96 % of our top-ranked candidate locations are actual fault locations. Keywords-multiple arbitrary faults; diagnosis; mask and reinforcement effect; fault-tuple equivalence tree I.