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

doi:10.12068/j.issn.1005-3026.2022.04.0012

Abstract

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

CLC Number:

TP460

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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