基于内嵌物理信息与注意力机制BiLSTM神经网络的臂架系统疲劳损伤预测模型

doi:10.3901/JME.2024.13.205

摘要/Abstract

摘要： 臂架系统是工程机械的关键承载部件，其结构安全决定了工程建设施工安全。结构疲劳损伤预测可保证臂架系统全生命周期服役安全，但用于预测的应力历程数据难以长期可靠获取，融合深度学习的间接信号反推方法是一种有效方式，但存在输入信号与疲劳损伤之间等效关系难以掌握、预测精度低等问题。针对上述问题，提出一种基于内嵌物理信息与注意力机制BiLSTM神经网络的臂架系统疲劳损伤预测模型。该模型突破了输入信号与疲劳损伤的高精等效映射难题，通过将物理模型与数据模型结果回归融合，并创新提出了一种全新的物理引导损失函数，显著提升了模型疲劳损伤预测能力。研究结果表明，该预测模型对不同工况下臂架系统的疲劳损伤均有较高的预测精度。

关键词: 物理信息, 深度学习, 臂架系统, 疲劳损伤, 注意力机制

Abstract: The boom system is a key load-bearing component of construction machinery, and its structural safety determines the safety of engineering construction. Structural fatigue damage prediction can ensure safe service throughout the life cycle of the boom system, but the stress data used for prediction is difficult to obtain reliably in the long term. Indirect signal backpropagation using deep learning is an effective method, but there are problems such as difficulty in grasping the equivalent relationship between input signals and fatigue damage, and low prediction accuracy. In response to the above issues, a fatigue damage prediction model is proposed for boom systems based on embedded physical information and attention mechanism BiLSTM neural network. Firstly, the overall framework of the model is introduced; Then, through a small number of actual working condition experiments, a data model of BiLSTM neural network based on a ttention mechanism is established, overcoming the problem of high-precision equivalent mapping between input signals and fatigue damage. Finally, by regressing and integrating the results of physical and data models, a novel physical guided loss function is innovatively proposed, significantly improving the model's fatigue damage prediction ability. The research results indicate that the prediction model has high prediction accuracy for fatigue damage of the boom system under different working conditions.

Key words: physical information, deep learning, boom system, fatigue damage, attention mechanism

中图分类号:

TB114.3

付玲, 佘玲娟, 颜镀镭, 张鹏, 龙湘云. 基于内嵌物理信息与注意力机制BiLSTM神经网络的臂架系统疲劳损伤预测模型[J]. 机械工程学报, 2024, 60(13): 205-215.

FU Ling, SHE Lingjuan, YAN Dulei, ZHANG Peng, LONG Xiangyun. Fatigue Damage Prediction Framework of The Boom System Based on Embedded Physical Information and Attention Mechanism BiLSTM Neural Network[J]. Journal of Mechanical Engineering, 2024, 60(13): 205-215.

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