A Multiple Attention Convolutional Neural Networks for Diesel Engine Fault Diagnosis

Fault diagnosis can improve the safety and reliability of diesel engines. An end-to-end method based on a multi-attention convolutional neural network (MACNN) is proposed for accurate and efficient diesel engine fault diagnosis. By optimizing the arrangement and kernel size of the channel and spatia...

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Bibliographic Details
Published in:Sensors (Basel, Switzerland) Switzerland), 2024-05, Vol.24 (9), p.2708
Main Authors: Yang, Xiao, Bi, Fengrong, Cheng, Jiangang, Tang, Daijie, Shen, Pengfei, Bi, Xiaoyang
Format: Article
Language:English
Subjects:
attention mechanism
Computational linguistics
Data processing
Deep learning
diesel engine
Diesel engines
Diesel motor
Efficiency
end-to-end fault diagnosis
Entropy
Fault diagnosis
Fourier transforms
Internal combustion engine industry
Language processing
machine learning
Methods
Natural language interfaces
Neural networks
Nuclear energy
self-attention mechanism
Signal processing
Valves
Wavelet transforms
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Summary:Fault diagnosis can improve the safety and reliability of diesel engines. An end-to-end method based on a multi-attention convolutional neural network (MACNN) is proposed for accurate and efficient diesel engine fault diagnosis. By optimizing the arrangement and kernel size of the channel and spatial attention modules, the feature extraction capability is improved, and an improved convolutional block attention module (ICBAM) is obtained. Vibration signal features are acquired using a feature extraction model alternating between the convolutional neural network (CNN) and ICBAM. The feature map is recombined to reconstruct the sequence order information. Next, the self-attention mechanism (SAM) is applied to learn the recombined sequence features directly. A Swish activation function is introduced to solve "Dead ReLU" and improve the accuracy. A dynamic learning rate curve is designed to improve the convergence ability of the model. The diesel engine fault simulation experiment is carried out to simulate three kinds of fault types (abnormal valve clearance, abnormal rail pressure, and insufficient fuel supply), and each kind of fault varies in different degrees. The comparison results show that the accuracy of MACNN on the eight-class fault dataset at different speeds is more than 97%. The testing time of the MACNN is much less than the machine running time (for one work cycle). Therefore, the proposed end-to-end fault diagnosis method has a good application prospect.
ISSN:1424-8220
1424-8220
DOI:10.3390/s24092708