Bearing Fault Diagnosis Based on Shallow Multi-Scale Convolutional Neural Network with Attention

Tengda Huang, Sheng Fu, Haonan Feng and Jiafeng Kuang
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Tengda Huang: Institute of Intelligent Monitoring and Diagnosis, Beijing University of Technology, Beijing 100124, China
Sheng Fu: Institute of Intelligent Monitoring and Diagnosis, Beijing University of Technology, Beijing 100124, China
Haonan Feng: Institute of Intelligent Monitoring and Diagnosis, Beijing University of Technology, Beijing 100124, China
Jiafeng Kuang: Institute of Intelligent Monitoring and Diagnosis, Beijing University of Technology, Beijing 100124, China

Energies, 2019, vol. 12, issue 20, 1-19

Abstract: Recently, deep learning technology was successfully applied to mechanical fault diagnosis. The convolutional neural network (CNN), as a prevalent deep learning model, occupies a place in intelligent fault diagnosis, which reduces the need for human feature extraction and prior knowledge, thereby achieving an end-to-end intelligent fault diagnosis model. However, the data for mechanical fault diagnosis in practical application are limited, the CNN model is too deep and too complex, making it prone to overfitting, and a model with too simple a structure and shallow layers cannot fully learn the effective features of the data. Convolutional filters with fixed window sizes are widely used in existing CNN models, which cannot flexibly select variable pivotal features. The model may be interfered with by redundant information in feature maps during training. Therefore, in this paper, a novel shallow multi-scale convolutional neural network with attention is proposed for bearing fault diagnosis. The shallow multi-scale convolutional neural network structure can fully learn the feature information of input data without overfitting. For the first time, a feature attention mechanism is developed for fault diagnosis to adaptively select features for classification more effectively, where the pivotal feature was emphasized, and the redundant feature was weakened through an attention mechanism. The time frequency representations as the input of the model were obtained from the vibration time domain signals, which contain the complete time domain and frequency domain information of the vibration signals. Compared with the current popular diagnostic methods, the results show that the proposed diagnostic method has fairly high accuracy, and its performance is superior to the existing methods. The average recognition accuracy was 99.86%, and the weak recognition rate of I-07 and I-14 labels was improved.

Keywords: Bearing fault diagnosis; multi-attention mechanism; multi-scale convolutional neural network; time frequency representation (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: 2019
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (4)

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