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应用深度学习识别法兰螺栓连接状态

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张洪,刘彬彬.应用深度学习识别法兰螺栓连接状态[J].,2021,40(3):350-357
应用深度学习识别法兰螺栓连接状态
The recognition of flange bolt connection state based on deep learning
投稿时间：2020-07-15修订日期：2021-04-27
中文摘要:
针对常规诊断方法对螺栓的连接状态识别效果差、鲁棒性和抗噪性弱等问题，提出了基于深度学习理论的螺栓检测新方法。首先以4种预紧力状态下的法兰螺栓结构产生的声发射信号为研究对象，借助于自适应噪声的完整集成经验模态分解理论以及梅尔频率倒谱系数特征提取方式，实现了声发射信号的自适应消噪和最优模态函数分量组的选取，提取到了可以较好分辨螺栓连接状态的梅尔频率倒谱系数特征值。通过训练模型，较好地对4种连接状态下的螺栓进行了识别。结果表明，该模型在法兰螺栓的声发射信号的诊断中，准确率高，具有较好的抗噪性和鲁棒性。
英文摘要:
Aiming at the problems of conventional diagnostic methods in identifying the connection state of bolts, weak robustness and noise resistance, a new method of bolt detection based on deep learning theory is proposed. Firstly, the acoustic emission signals generated by the flange bolt structure under four kinds of preloading force are taken as the research object, relying on the complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN), and the feature extraction method of Mel-frequency cepstral coefficients (MFCC).The adaptive noise reduction of the acoustic emission signal and the selection of the optimal basic mode component group are realized. The eigenvalues of Mel-frequency cepstral coefficients that can better distinguish the bolt connection state are extracted. Through the training model, the bolts in the four connection states are well identified. The results show that the model has high accuracy in acoustic emission diagnosis of flange bolts, and has good noise resistance and robustness.
DOI：10.11684/j.issn.1000-310X.2021.03.005
中文关键词:法兰螺栓声发射梅尔频率倒谱系数深度学习状态识别
英文关键词:Flange boltAcoustic emissionMel-frequency cepstral coefficientsDeep learningState recognition
基金项目:
作者单位E-mail
张洪江南大学1105399774@qq.com
刘彬彬江南大学laubenben@163.com
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