<abstract xmlns="http://www.w3.org/1999/xhtml">

<p>Marine centrifugal fans usually work in harsh environments. Their vibration signals are non-linear. The traditional fault diagnosis methods of fans require much calculation and have low operating efficiency. Only shallow fault features can be extracted. As a result, the diagnosis accuracy is not high. It is difficult to realize the end-to-end fault diagnosis. Combining the Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN) and lightweight neural network, a fault classification method is proposed. First, the CEEMDAN can decompose the vibration signal into several intrinsic modal functions (IMF). Then, the original signals can be transformed into 2-D images through pseudo-colour coding of the IMFs. Finally, they are fed into the lightweight neural network for fault diagnosis. By embedding a convolutional block attention module (CBAM), the ability of the network to extract critical feature information is improved. The results show that the proposed method can adaptively extract the fault characteristics of a marine centrifugal fan. While the model is lightweight, the overall diagnostic accuracy can reach 99.3%. As exploratory basic research, this method can provide a reference for intelligent fault diagnosis systems on ships.</p>
</abstract>

Marine centrifugal fans usually work in harsh environments. Their vibration signals are non-linear. The traditional fault diagnosis methods of fans require much calculation and have low operating efficiency. Only shallow fault features can be extracted. As a result, the diagnosis accuracy is not high. It is difficult to realize the end-to-end fault diagnosis. Combining the Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN) and lightweight neural network, a fault classification method is proposed. First, the CEEMDAN can decompose the vibration signal into several intrinsic modal functions (IMF). Then, the original signals can be transformed into 2-D images through pseudo-colour coding of the IMFs. Finally, they are fed into the lightweight neural network for fault diagnosis. By embedding a convolutional block attention module (CBAM), the ability of the network to extract critical feature information is improved. The results show that the proposed method can adaptively extract the fault characteristics of a marine centrifugal fan. While the model is lightweight, the overall diagnostic accuracy can reach 99.3%. As exploratory basic research, this method can provide a reference for intelligent fault diagnosis systems on ships.

Deep Learning-Based Fault Diagnosis for Marine Centrifugal Fan

Polish Maritime Research

This work is licensed under the Creative Commons Attribution 4.0 International License.

{"article-title":"Deep Learning-Based Fault Diagnosis for Marine Centrifugal Fan"}

Marine centrifugal fans usually work in harsh environments. Their vibration signals are non-linear. The traditional fault diagnosis methods of fans require...

Deep Learning-Based Fault Diagnosis for Marine Centrifugal Fan

Publié en ligne: 19 avr. 2023

Pages: 112 - 120

DOI: https://doi.org/10.2478/pomr-2023-0011

Mots clés
CEEMDAN, Lightweight neural network, Marine centrifugal fan, Fault diagnosis

© 2023 Congyue Li et al., published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

Deep Learning-Based Fault Diagnosis for Marine Centrifugal Fan

Publié en ligne: 19 avr. 2023

Pages: 112 - 120

DOI: https://doi.org/10.2478/pomr-2023-0011

Mots clésCEEMDAN, Lightweight neural network, Marine centrifugal fan, Fault diagnosis

© 2023 Congyue Li et al., published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

Mots clés
CEEMDAN, Lightweight neural network, Marine centrifugal fan, Fault diagnosis