A Fault Diagnosis Strategy for Analog Circuits Based on Composite Neural Networks

Analog circuit fault diagnosis is a technology that leads and has basic significance in electronic engineering and is vital in ensuring the maintainability and safety of electronic systems. This paper presents a fault diagnosis based on composite neural networks. The method used allows for the benefits of the feedforward neural network and the recurrent neural networks to be absorbed, while learning some static and dynamic characteristics of the behavior of the circuit. A precondition for success in this method is the introduction of an adjustable weighting schema used in weighting the importance of the components in the network. This enhances the accuracy and reliability of the diagnosis process. The experimental results show that the composite neural network diagnostic method has achieved remarkable performance for different types of faults, thus proving its superiority as a fault diagnosis method compared with the standard neural network model. In addition, this composite neural network possesses wide compatibility, which is well demonstrated in this paper, indicating that it can be applied in fault diagnosis for different circuit topologies and working environments.