服役结构超前载荷实时预测方法的研究

doi:10.12068/j.issn.1005-3026.2022.04.0012

东北大学学报（自然科学版） ›› 2022, Vol. 43 ›› Issue (4): 541-550.DOI: 10.12068/j.issn.1005-3026.2022.04.0012

服役结构超前载荷实时预测方法的研究

杨博文，霍军周，张伟，张占葛

(大连理工大学机械工程学院，辽宁大连116024)

修回日期:2021-06-10 接受日期:2021-06-10 发布日期:2022-05-18
通讯作者: 杨博文
作者简介:杨博文(1992-)，女，辽宁本溪人，大连理工大学博士研究生；霍军周(1979-)，男，山西运城人，大连理工大学教授，博士生导师.
基金资助:
国家自然科学基金资助项目(51875076)；国家重点研发计划项目(2018YFB1306701)；辽宁百千万人才计划项目(2020921006)；国家自然科学基金辽宁省联合基金资助项目(U1708255).

Research on Real-Time Overload Prediction Method of in-Service Structures

YANG Bo-wen， HUO Jun-zhou， ZHANG Wei， ZHANG Zhan-ge

School of Mechanical Engineering， Dalian University of Technology， Dalian 116024， China.

Revised:2021-06-10 Accepted:2021-06-10 Published:2022-05-18
Contact: HUO Jun-zhou
About author:-
Supported by:
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摘要/Abstract

摘要： 为保证关键结构疲劳寿命的实时监测，以动态随机载荷为监测条件，准确预测超前载荷谱对实际工程分析十分重要.针对服役设备难以实时监测并准确反应载荷真实规律等问题，提出一种基于数值分析的概率密度预测方法，结合机器学习BP神经网络智能算法建立预测模型.应变传感器采集随机载荷进行预处理得到随机载荷谱，利用蒙特卡洛法分析模型载荷波形走势及波动范围的预测精度.结果表明:超前预测载荷谱的核密度拟合曲线与真实数值相似性较大，为大型复杂服役结构件的超前载荷监测提供了理论支持与实际工程应用.

关键词: 超前载荷预测;BP神经网络;蒙特卡洛法;核密度估计;实时预测

Abstract: In order to ensure the real-time monitoring of the fatigue life of key structures， the dynamic random load is used as the monitoring condition to accurately predict the importance of the advanced load spectrum for actual engineering analysis.Aiming at the difficulty of real-time monitoring of in-service equipment and accurately responding to the real laws of load， a probability density prediction method based on numerical analysis is proposed， combined with machine learning BP neural network intelligent algorithm to establish a prediction model. The random load is collected by the strain sensor for preprocessing to obtain random load spectra， and the Monte Carlo method is used to analyze the model load waveform trend and the prediction accuracy of the fluctuation range. The results show that the nuclear density fitting curve of the advanced prediction load spectrum has a high similarity to the real value， which provides theoretical support and practical engineering application for the advanced load monitoring of large and complex in-service structures.

Key words: advanced load prediction; BP neural network; Monte Carlo method; kernel density estimation; real-time prediction

中图分类号:

TP460

杨博文，霍军周，张伟，张占葛. 服役结构超前载荷实时预测方法的研究[J]. 东北大学学报（自然科学版）, 2022, 43(4): 541-550.

YANG Bo-wen， HUO Jun-zhou， ZHANG Wei， ZHANG Zhan-ge. Research on Real-Time Overload Prediction Method of in-Service Structures[J]. Journal of Northeastern University(Natural Science), 2022, 43(4): 541-550.

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服役结构超前载荷实时预测方法的研究

Research on Real-Time Overload Prediction Method of in-Service Structures

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