A Fault Diagnosis Method Based on a Rainbow Recursive Plot and Deep Convolutional Neural Networks

Xiaoyuan Wang, Xin Wang (), Tianyuan Li and Xiaoxiao Zhao
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Xiaoyuan Wang: School of Electrical and Information Engineering, Tianjin University, Tianjin 300072, China
Xin Wang: School of Electrical and Information Engineering, Tianjin University, Tianjin 300072, China
Tianyuan Li: School of Electrical and Information Engineering, Tianjin University, Tianjin 300072, China
Xiaoxiao Zhao: School of Electrical and Information Engineering, Tianjin University, Tianjin 300072, China

Energies, 2023, vol. 16, issue 11, 1-15

Abstract: In previous deep learning-based fault diagnosis methods for rotating machinery, the method of directly feeding one-dimensional data into convolutional neural networks can lead to the loss of important fault features. To address the problem, a novel rotating machinery fault diagnosis model based on a rainbow recursive plot (RRP) is proposed. Our main innovation and contributions are: First, a RRP is proposed to convert the one-dimensional vibration signal from the rotating machinery into a two-dimensional color image, facilitating the capturing of more significant fault information. Second, a new CNN based on LeNet-5 is devised, which extracts a feature that describes substantial fault information from the converted two-dimensional color image, thus performing fault diagnosis recognition accurately. The public rolling bearing datasets and the online fault diagnosis platform are adopted to verify proposed method performance. Experiments on public datasets show that the proposed method can improve the accurate rate of recognition to 97.86%. More importantly, online experiment on the self-made fault diagnosis platform demonstrates that our approach achieves the best comprehensive performance in terms of recognition speed and accuracy compared to mainstream algorithms.

Keywords: fault diagnosis; rainbow recursive plot; convolutional neural network; online; experiment platform (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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