基于多层特征融合高斯CDBN的铝合金铆接板内部故障诊断

Liang Liu, Chenyang Shi, Hengyi Zou, Hui Song
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引用次数: 0

摘要

由于燃料经济性的考虑,轻型材料的应用在飞机和汽车工业中得到了广泛的应用。为了保证车身铆接的质量,有必要建立一种检测铆接内部缺陷的无损检测方法。提出了一种基于改进高斯卷积深度信念网络(CDBN)的内铆接故障诊断算法。首先,提出特征提取方法对涡流传感器采集到的检测数据进行处理,提高数据分析效率;其次,利用高斯可见单元取代网络模型的二进制可见单元,增强网络模型的特征表示能力;最后,采用多层特征融合方法,提高模型各层的特征学习能力。将该算法应用于铆接内部故障的检测。实验结果表明,与基于DBN、标准CDBN和卷积神经网络的方法相比,该算法能有效提高铆接故障的识别率。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Internal Fault Diagnosis of Riveted Aluminum Alloy Plates Based on Gaussian CDBN with Multi-layer Feature Fusion
The application of lightweight material jointing is popular for aircraft and automotive industry due to fuel economy. In order to ensure the quality of the body riveting, it is essential to develop a nondestructive testing (NDT) method to identify the internal riveting defects. The paper presents a diagnosis algorithm for internal riveting faults by improved Gaussian convolutional deep belief network (CDBN). Firstly, the feature extraction method is proposed to process the detection data collected from the eddy current sensor to improve the efficiency of data analysis. Secondly, Gaussian visible units are used to replace the binary visible units of the network model to enhance the feature representation ability. Finally, the multi-layer feature fusion method is employed to improve the feature learning ability of each layer of the model. The algorithm is applied to the detection of riveting internal faults. The experimental results reflect that the algorithm can effectively improve the recognition rate of riveting faults compared with the methods based on DBN, standard CDBN and convolutional neural network.
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