压缩特征驱动下贝叶斯神经网络对复材板损伤的评估预测

doi:10.3901/JME.2025.12.001

机械工程学报 ›› 2025, Vol. 61 ›› Issue (12): 1-11.doi: 10.3901/JME.2025.12.001

• 仪器科学与技术 • 上一篇

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压缩特征驱动下贝叶斯神经网络对复材板损伤的评估预测

刘小峰, 周曾亮, 张春兵, 柏林

重庆大学高端装备机械传动全国重点实验室重庆 400044

收稿日期:2024-07-12 修回日期:2025-01-16 发布日期:2025-08-07
作者简介:刘小峰(通信作者)，女，1980年出生，博士，教授，博士研究生导师。主要研究方向为工程信号处理、设备监测与故障诊断，智能测试与仪器。E-mail：liuxfeng0080@126.com;周曾亮，男，2000年出生。主要研究方向为要研究方向为超声导波无损检测。E-mail：18185047545@163.com;张春兵，男，1998年出生，博士研究生。主要研究方向为工程信号处理。E-mail：zhangchunbing1@163.com;柏林，男，1972 年出生，博士，副教授。主要研究方向为机械设备状态监测与故障诊断。E-mail：bolin000l@aliyun.com
基金资助:
国家科技重大专项(J2019-IV-0001-0068)和国家自然科学基金(52175077)资助项目。

Squeezing Feature-driven Bayesian Neural Network for Damage Evaluation and Prediction of Composite Plate

LIU Xiaofeng, ZHOU Zengliang, Zhang Chunbing, Bo Lin

State Key Laboratory of Mechanical Transmission for Advanced Equipment, Chongqing University, Chongqing 400044

Received:2024-07-12 Revised:2025-01-16 Published:2025-08-07

摘要/Abstract

摘要： 针对复合材料层合板疲劳损伤扩展的预测问题，提出一种基于Lamb波压缩特征驱动的贝叶斯神经网络模型的损伤评估与预测方法。采用Lamb波信号时频域特征、动态时间规整特征及传递熵特征等多域特征构建复材板的损伤状态空间，结合挤压-激励网络构建统一压缩特征对复材板损伤程度进行量化表征。在复材板应变能释放率模型的基础上,建立损伤扩展速率与应变能量释放率变化(Change of strain energy release rate，CSERR)之间的线性关系。将CSERR嵌入至贝叶斯神经网络(Bayesian neural network,BNN)中的激活函数中，构建一种CSERR-BNN预测模型，并对层合板不同损伤的演化趋势进行评估预测。有限元仿真及T700G单向碳纤维预浸料制成的层合板试验数据分析结果表明，以Lamb波特征压缩构建的统一损伤指数作为驱动，采用CSERR-BNN预测模型能有效预测损伤状态参量的演化趋势，实现复材板的损伤状态追踪与剩余寿命评估。

关键词: 超声导波, 复合材料层合板, 应变能释放率模型, 贝叶斯估计, 寿命预测

Abstract: Aiming at the problem of predicting the fatigue damage extension of composite laminates, a damage assessment and prediction method is proposed based on the Bayesian neural network model driven by Lamb wave squeezing features. Initially, the damage state space of the composite plate is constructed from the multi-domain features, including time-frequency domain features of Lamb wave signals, dynamic time warping features, and transfer entropy features. In order to quantitatively characterize the damage degree of the composite plate, a unified compression feature(UCF) is constructed by using the Squeezing-excitation network. Then, on the basis of the strain energy release rate model of composite laminate, the damage expansion rate and the change of strain energy release rate(CSERR) are found to be linearly related. After that, the CSERR-BNN prediction model is created by embedding the CSERR into the activation function of Bayesian neural network(BNN). This model is then utilized to evaluate and predict the evolution trends of multiple damages in the laminate. The results of finite element simulation and test data analysis of laminates made of T700G unidirectional carbon fiber prepreg showed that the CSERR-BNN prediction model, driven by the UCFs of multi-channel Lamb wave signals, can accurately predict the damage state progression trend in composite laminates with accuracy, thus tracking of the damage state and assessing their remaining useful life.

Key words: ultrasonic guided wave, composite laminates, energy release rate model, Bayesian estimation, life prediction

中图分类号:

TH39
V219

刘小峰, 周曾亮, 张春兵, 柏林. 压缩特征驱动下贝叶斯神经网络对复材板损伤的评估预测[J]. 机械工程学报, 2025, 61(12): 1-11.

LIU Xiaofeng, ZHOU Zengliang, Zhang Chunbing, Bo Lin. Squeezing Feature-driven Bayesian Neural Network for Damage Evaluation and Prediction of Composite Plate[J]. Journal of Mechanical Engineering, 2025, 61(12): 1-11.

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压缩特征驱动下贝叶斯神经网络对复材板损伤的评估预测

Squeezing Feature-driven Bayesian Neural Network for Damage Evaluation and Prediction of Composite Plate

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