Research on Ship Fault Diagnosis Based on Deep Learning

Abstract

This paper proposes a deep learning algorithm to diagnose ship faults in order to improve the accuracy and diagnostic efficiency of ship fault diagnosis. 90% of the large number of unlabeled ship operational data samples are selected for model training, and the remaining 10% is used for model testing. We optimize model parameters and improve the accuracy of the deep learning model for fault diagnosis classification. Hidden layer functions are used to extract multi-layer data features and perform feature fusion. Gain values are used to define ship faults, primary faults, secondary faults, and tertiary faults. Finally, we use the soft-max classifier to fault output the fault and get the fault level output. The experimental results on the ship simulation fault dataset show that compared with other traditional algorithms, the accuracy of the fault diagnosis of this method is greatly improved, and the simulation result is 92.5%. Experiments show that the model based on deep learning algorithm for multi-layer feature fusion training can better meet the needs of ship fault diagnosis under complex systems.