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A Real-Time Fault Location Mechanism Combining CGP Code and Deep Learning

The rapid increase in the scale and complexity of the circuit system has led to serious problems in safety and reliability. Therefore, fault tolerance was proposed. Fault location as part of fault tolerance is indispensable. However, fault location methods are mostly limited to small data volume and high system complexity. How to achieve the fault location of the circuit system has always been a focus question. This paper proposes a Hierarchical Multi-module Fault Location Mechanism (HMFLM). Cartesian Genetic Programming (CGP) is exploited to generate circuits and random injects faults into it. The model matching library is used to store the training model of the layering module circuit and detect circuit faults in real time. The recovery priority of the fault circuits utilize Fault Analysis Tree (FAT) to determine, therefore, we can effectively facilitate fault recovery. The results show HMFLM can effectively locate multiple faults and improves the real-time and reliability of fault diagnosis.