首页|基于递归量化分析的CFRP超声检测缺陷识别方法

基于递归量化分析的CFRP超声检测缺陷识别方法

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为了解决碳纤维增强复合材料(CFRP)超声检测缺陷识别不准确、不可靠的问题，提出自适应变分模态分解(AVMD)与递归量化分析技术(RQAT)特征提取和卷积神经网络(CNN)识别方法。实验预埋 6 种模拟缺陷，使用超声相控阵检测后，每种缺陷取 500 个A扫描波形信号数据，利用蝠鲼智能优化算法优化出变分模态分解(VMD)所需的K、Alpha，使用优化参数的VMD得到本征模态函数(IMF)分量，筛选高频噪声部分，对剩余IMF分量使用递归量化分析技术。每个信号得到 72 个特征值，将特征值组成特征向量，输入CNN进行识别，训练集识别正确率为 99。94%，验证集识别正确率为 98。09%，测试集识别正确率为 98。27%。结果表明，AVMD与RQAT、CNN的结合解决了CFRP超声检测中缺陷的识别分类问题。
CFRP ultrasonic detection defect identification method based on recursive quantitative analysis
An adaptive variational mode decomposition(AVMD)and recursive quantitative analysis technique(RQAT)for feature extraction was proposed combined with convolutional neural network(CNN)for recognition in order to address the issues of inaccuracy and unreliability in ultrasonic defect detection of carbon fiber reinforced plastics(CFRP).Six types of simulated defects were embedded in the experiments,and 500 A-scan waveform signals were collected for each defect type after ultrasonic phased array detection.The stingray intelligent optimization algorithm was used to optimize the K and Alpha values required for variational mode decomposition(VMD).Intrinsic mode function(IMF)components were obtained by using these optimized parameters,and high-frequency noise parts were filtered out.The remaining IMF components were processed with recursive quantitative analysis technique.Each signal yielded 72 feature values,which were assembled into feature vectors and input into the CNN for recognition.The recognition accuracy was 99.94%for the training set,98.09%for the validation set,and 98.27%for the test set.Results show that the combination of AVMD,RQAT and CNN solves the defect recognition and classification problem in CFRP ultrasonic testing.

carbon fiber reinforced plastics(CFRP)non-destructive testingvariational mode decompositionrecursive quantitative analysisfeature extractionconvolutional neural networkdefect identification

王海军、王涛、俞慈君

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浙江大学工程师学院,浙江杭州 310058

浙江大学机械工程学院,浙江杭州 310058

碳纤维复合材料(CFRP) 无损检测 变分模态分解 递归量化分析 特征提取 卷积神经网络 缺陷识别

自然科学基金重点资助项目国家自然科学基金创新研究群体科学基金资助项目浙江省重点研发计划资助项目

91748204518210932020C01039

2024

10.3785/j.issn.1008-973X.2024.08.008

浙江大学学报(工学版)
浙江大学

浙江大学学报(工学版)

CSTPCD北大核心
影响因子：0.625
ISSN：1008-973X
年,卷(期)：2024.58(8)
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