A short time series rolling bearing fault diagnosis method based on FMTF-CNN

• Published in: Engineering Research Express Vol. 6; no. 2; pp. 25346 - 25360
• Main Authors: Chen, Xuejiang, Zhang, Yansong, Su, Yang, Zhou, Yan, Gong, Wuqi
• Format: Journal Article
• Language: English
• Published: IOP Publishing 01.06.2024
• Subjects: convolutional neural network; fault diagnosis; fourier transform; markov transition field; short time sequence vibration signal
• ISSN: 2631-8695; 2631-8695
• DOI: 10.1088/2631-8695/ad4957

The signal characteristics are extracted directly from the convolutional level when the Convolutional Neural Network (CNN) is used as a fault diagnosis method in most instances. Some scholars have proposed to use the Markov Transition Field (MTF) to extract the signal characteristics before convolution. However, for time-domain signals, the fault characteristics extraction is difficult, and the training process of the neural network parameters is slow. Therefore, in this paper, based on Fast Fourier Transform (FFT) and MTF algorithm, the Fourier Markov Transition Field (FMTF) characteristics extraction method in frequency domain is proposed, and a large number of experiments are carried on the public bearing data set of Jiangnan University. The final verification shows that the FMTF-CNN method has higher accuracy, faster training process and more accurate characteristics extraction than the traditional MTF-CNN diagnosis method.