Model-based diagnosis is an automatic fault detection approach in artificial intelligence. It is used in software fault detection and hardware diagnosis. Recently

the difficulty of circuit diagnosis is increasing with the increasing size and complexity of the circuit. After studying the characteristics of the circuit model

this paper proposes the grouped diagnosis (GD) approach based on the LLBRS-tree (Last-Level Based on Reverse Search-tree) algorithm. Firstly

the component grouped method is used to identify the component's faulty correlation and group the components. And the scale of the circuit to be detected can be reduced. Secondly

through the grouped circuit

the non-diagnostic solution theorem is given to locate some non-diagnostic solutions with the feature of satisfiability. It can help us avoid checking consistency on these non-diagnostic solutions so as to accelerate the processing. Our algorithm can be used in electronic circuit fault diagnosis. And the experimental results show that it improves the efficiency by 1.5 times and even 3 times compared with the LLBRS-tree algorithm.