Combined Optimization of Neural Network Fault Diagnosis Methods for Analog Circuits on Ships

With increasing automation, electronic systems are becoming more widely used on ships. When electronic systems fail, it will greatly affect the safety of ship operation or even cause catastrophic accidents. Therefore, it is necessary to diagnose a circuit fault in order to repair the equipment in time. In electronic systems, more than 80% of the faults come from analog circuits. To obtain better fault diagnosis accuracy for analog circuits, neural network classification algorithms are often combined with optimization steps, such as circuit feature collection, feature engineering, regularization and so on, significantly affecting the accuracy of the classification algorithm, such as circuit feature collection, feature engineering, and regularization. However, the accuracy of analog circuit fault diagnosis is often not high in practical applications because of the tolerance of analog circuit components and other factors. To solve the above problems, this paper proposes a neural network combination optimization method based on hypothesis testing and applies it this method to the fault diagnosis of analog circuits on ships. This method uses hypothesis testing to combine and sort the optimization methods of each step in a neural network classification algorithm to obtain a model ranking based on the accuracy rate with mathematical statistics rules, thereby obtaining the optimal combination method. Finally, the validity of the method is verified by experiments.