An Intelligent Fault Diagnosis Method of Rolling Bearings Based on Short-Time Fourier Transform and Convolutional Neural Network

doi:10.1007/s11668-023-01616-9

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	An Intelligent Fault Diagnosis Method of Rolling Bearings Based on Short-Time Fourier Transform and Convolutional Neural Network
	Zhang, Qi; Deng, Linfeng
	2023-02
发表期刊	JOURNAL OF FAILURE ANALYSIS AND PREVENTION
ISSN	1547-7029
摘要	The rolling bearing is the key component of rotating machinery, and fault diagnosis for rolling bearings can ensure the safe operation of rotating machinery. Fault diagnosis technology based on deep learning has been largely studied for bearing fault diagnosis. However, for the deep learning model based on convolutional neural network, there are some intrinsic problems of producing inconspicuous features and useful feature information loss in the process of feature extraction of the raw fault vibration signals. In this work, an intelligent fault diagnosis method of rolling bearings based on short-time Fourier transform and convolutional neural network (STFT-CNN) is proposed. The one-dimensional vibration signals are converted into time-frequency images by STFT. Then, time-frequency images are inputted into STFT-CNN model for fault feature learning and fault identification. For the STFT of the vibration signals, the window type, window width and translation overlap width of the five typical window functions are studied and optimal one is obtained. And in the STFT-CNN model, the stacked double convolutional layers are adopted to improve the nonlinear expression capability of the model. To verify the effectiveness of the proposed method, experiments are carried out on the Case Western Reserve University (CWRU) and the Machine Failure Prevention Technology (MFPT) Society bearing datasets. The results show that the proposed method outperforms other comparative methods and reaches the identification accuracy of 100% and 99.96% for CWRU and MFPT, respectively.
关键词	Rotating machinery Rolling bearing Intelligent fault diagnosis Convolutional neural network Short-time Fourier transform
DOI	10.1007/s11668-023-01616-9
收录类别	ESCI ; EI
语种	英语
WOS研究方向	Engineering
WOS类目	Engineering, Multidisciplinary
WOS记录号	WOS:000928896900001
出版者	SPRINGERNATURE
EI入藏号	20230613570246
来源库	WOS
引用统计	被引频次[WOS]：0 [WOS记录] [WOS相关记录]
文献类型	期刊论文
条目标识符	https://ir.lut.edu.cn/handle/2XXMBERH/161193
专题	法学院机电工程学院
作者单位	Lanzhou Univ Technol, Sch Mech & Elect Engn, Lanzhou, Peoples R China
第一作者单位	兰州理工大学
第一作者的第一单位	兰州理工大学
推荐引用方式 GB/T 7714	Zhang, Qi,Deng, Linfeng. An Intelligent Fault Diagnosis Method of Rolling Bearings Based on Short-Time Fourier Transform and Convolutional Neural Network[J]. JOURNAL OF FAILURE ANALYSIS AND PREVENTION,2023.
APA	Zhang, Qi,&Deng, Linfeng.(2023).An Intelligent Fault Diagnosis Method of Rolling Bearings Based on Short-Time Fourier Transform and Convolutional Neural Network.JOURNAL OF FAILURE ANALYSIS AND PREVENTION.
MLA	Zhang, Qi,et al."An Intelligent Fault Diagnosis Method of Rolling Bearings Based on Short-Time Fourier Transform and Convolutional Neural Network".JOURNAL OF FAILURE ANALYSIS AND PREVENTION (2023).