Intelligent identification and prediction methods for ship structural damage
To address the issues of low efficiency and accuracy in traditional ship structural damage identification,an intelligent diagnostic recognition and prediction model suitable for ship structures is proposed.Initially,preprocess the col-lected vibration impact signals and conduct feature analysis and extraction in the time domain.Subsequently,utilizing a large dataset,an improved convolutional neural network(CNN)model is employed for the classification and identification of ship structural damage types and severity.An ARIMA time series model is applied to predict the time of ship structural damage.This approach enables timely warning and advance knowledge of the health status of ship structures,providing a reference for proactive maintenance.Practical case applications demonstrate that the proposed ship structural damage identification and prediction method based on the improved CNN and ARIMA models can effectively utilize big data from ship structural health monitoring,significantly enhancing the efficiency and accuracy of fault identification.It offers a viable approach for intelligent operation and health management of maritime equipment.
万方数据
维普
