摘要
传统的轴承故障诊断方法非常依赖于研究者的特征提取经验和分类器的参数选择。卷积神经网络存在训练时间长和诊断精度低的问题无法满足高精度设备管理要求。为了提高诊断精度并降低训练时间,本文提出一种有效的轴承故障检测方法。该方法基于FFT和全连接层提取的故障特征能够有效帮助SVM分类器进行分类。凯斯西储大学的开源轴承数据被应用于检测该方法的有效性。该方法可以准确对不同轴承工作状态进行分类,并具有一定程度的鲁棒性。当全部测试集被加入噪音后,依然能够得到99%以上的诊断准确率。实验结果表明与传统方法相比,该方法不但能够提高分类准确精度以达到高精度设备的要求,并且能够大幅降低模型训练时间。
The conventional intelligent bearing fault diagnosis method relies on the experience of feature extraction and the parameter selection of the classifier.The training time of convolutional neural network is long and the accuracy cannot meet the requirements of high-precision mechanical equipment.To improve the accuracy and reduce the training time,an effective bearing fault diagnosis method is proposed in this paper.Firstly the signal is transformed by FFT.Secondly fully connected layer is used for feature extraction.SVM classification is adopted in the last step for condition classification.The experiments verify that the proposed model in this paper is effective for the fault diagnosis of rolling bearing under different working conditions.The model has an appropriate degree of robustness.99%classification accuracy could still be reached after noise is added to all the test set.The results show that the proposed method can not only improve the classification accuracy to meet the diagnosis requirements of high-precision mechanical equipment,but also greatly reduce the training time compared with the conventional methods.
作者
王萌
曾艳
刘金童
刘小杰
彭飞
Meng WANG;Yan ZENG;Jin-tong LIU;Xiao-jie LIU;Fei PENG(Tangshan Polytechnic College,Tangshan 063299,China;Zhejiang University,Hangzhou 310058,China;North China University of Science and Technology,Tangshan 063210,China)
出处
《机床与液压》
北大核心
2020年第24期188-196,共9页
Machine Tool & Hydraulics
基金
国家自然科学基金项目(51775495)
河北省高等教育技术研究项目(QN2019200)
唐山市应用基础计划研究项目(19130217g)。
