基于CWT-CNN的滚动轴承故障诊断  

Fault Diagnosis of Rolling Bearing Based on CWT-CNN

作　　者：宋乾坤 周孟然[2] SONG Qiankun;ZHOU Mengran(School of Artificial Intelligence,Anhui University of Science and Technology,Anhui Huainan 232001,China;School of Electrical and Information Engineering,Anhui University of Science and Technology,Anhui Huainan 232001,China)

机构地区：[1]安徽理工大学人工智能学院,安徽淮南232001 [2]安徽理工大学电气与信息工程学院,安徽淮南232001

出　　处：《重庆工商大学学报（自然科学版）》2023年第3期42-47,共6页Journal of Chongqing Technology and Business University:Natural Science Edition

摘　　要：针对滚动轴承传统故障诊断方法训练收敛速度慢、识别准确率不高、抗噪性能差等问题,提出CWT-CNN的轴承故障诊断模型。通过对滚动轴承振动数据经连续小波变换生成的时频图进行三次垂直方向随机裁剪的方法扩充数据集,之后将其导入到搭建的加入了批量归一化和随机失活的卷积神经网络中进行模型训练,再由训练好的模型实现轴承故障分类。为了测试模型性能,使用凯斯西储大学轴承数据集进行检测,经过实验结果表明:基于提出的方法构建的数据集相比于常规方法,在搭建的卷积神经网络训练中收敛速度更快,训练出的模型性能也更加稳定,最终最高测试准确率为99.75%,常规方法构建的数据集准确率为99.67%,证明了构建数据集方法的可行性;在原始数据中加入信噪比为6dB高斯白噪声后,通过常规方法构建的数据集测试的最高准确率仍达到了98.67%,展现了基于CWT-CNN的轴承故障诊断模型较强的抗噪能力,证明了所提方法的有效性和可行性。A bearing fault diagnosis model based on CWT-CNN was proposed to solve the problems of slow training convergence speed,low recognition accuracy and poor anti-noise performance of traditional fault diagnosis methods for rolling bearings.The data set was expanded by three vertical random clippings of the time-frequency graph generated by continuous wavelet transform of rolling bearing vibration data,then it was imported into the constructed convolutional neural network with batch normalization and random inactivation for model training,and the trained model was used to realize bearing fault classification.To test the model performance,the Case Western Reserve University bearing dataset was used for detection.The experimental results show that compared with the data set constructed by the conventional method,the data set constructed by the proposed method has a faster convergence speed in the training of the constructed convolutional neural network,the performance of the trained model is also more stable,and the final test is accurate.The final highest test accuracy of this data set is 99.75%,and the accuracy rate of the dataset constructed by the conventional method is 99.67%,which proves the feasibility of the method in constructing the dataset.After adding white Gaussian noise with a signal-to-noise ratio of 6dB to the original data,the highest accuracy rate of the data set constructed by the conventional method still reaches 98.67%,showing the strong anti-noise ability of the bearing fault diagnosis model based on CWT-CNN,which proves the effectiveness and feasibility of the proposed method.

关 键 词：故障诊断 连续小波变换 卷积神经网络 随机裁剪 抗噪能力 

分 类 号：TP181[自动化与计算机技术—控制理论与控制工程]