期刊文献+

船体结构损伤的智能识别与预测方法

Intelligent identification and prediction methods for ship structural damage
下载PDF
导出
摘要 为解决传统的船体结构损伤识别效率低下、准确性不高等问题,提出一种适用于船体结构的智能诊断识别与预测模型。首先对采集到的振动冲击信号进行预处理并在时域维度进行特征分析和提取。然后利用大量样本数据,采用改进的卷积神经网络(CNN)模型对船体结构损伤类型和严重程度进行分类识别,采用ARIMA时间序列模型对船体结构损伤时间进行预测,进而实现船体结构健康状态的及时预警和提前预知,为船体结构预防性维修提供参考。通过实际案例应用表明,提出的基于改进卷积神经网络和ARIMA模型的船体结构损伤识别和预测方法,可以充分利用船体结构健康监测大数据,有效提高故障的识别效率和准确率,为船舶装备的智能运维和健康管理提供可行的途径。 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.
作者 宋庭新 干永明 韩国晨 SONG Tingxin;GAN Yongming;HAN Guochen(School of Mechanical Engineering,Hubei University of Technology,Wuhan 430068,China)
出处 《舰船科学技术》 北大核心 2024年第19期170-175,共6页 Ship Science and Technology
基金 国家重点研发计划资助项目(2018YFF0214705)。
关键词 损伤识别 故障预测 CNN模型 ARIMA模型 船体结构 damage identification fault prediction CNN model ARIMA mode hull structure
  • 相关文献

相关作者

内容加载中请稍等...

相关机构

内容加载中请稍等...

相关主题

内容加载中请稍等...

浏览历史

内容加载中请稍等...
;
使用帮助 返回顶部