计算机科学 ›› 2021, Vol. 48 ›› Issue (11A): 648-654.doi: 10.11896/jsjkx.210100161
刘鑫1, 黄沁元1,2, 李强1, 冉茂霞1, 周颖1, 杨天1
LIU Xin1, HUANG Qin-yuan1,2, LI Qiang1, RAN Mao-xia1, ZHOU Ying1, YANG Tian1
摘要： 磁瓦作为永磁电机中的关键部件,其产品质量易受到内部缺陷的影响而下降。然而传统的声振检测手段在面对快速、精准的检测需求下已暴露出一些低效率的问题,因此开发一种针对磁瓦内部缺陷的高效智能化检测方法具有重要的现实意义。文中结合深度学习的优势,提出了一种基于卷积神经网络的磁瓦内部缺陷声振检测方法。在该方法中,磁瓦的一维声振信号首先被转换为二维声振图像,再输入针对信号特点所设计的卷积神经网络进行学习训练,以完成从声振图像中自主学习和提取能区分内部缺陷有无的信号特征,最后由softmax完成对应特征的识别。4类磁瓦样本的检测实验结果表明,提出的方法可实现准确率为99.38%的磁瓦内部缺陷检测,单片磁瓦的检测时间低于0.031 s,模型具有良好的鲁棒性。
中图分类号:
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