Deep learning-based fault diagnosis for marine centrifugal fan

• Autorzy: Li Congyue , Hu Yihuai , Jiang Jiawei , Yan Guohua

Identyfikatory

DOI

10.2478/pomr-2023-0011

• Języki publikacji: EN

Abstrakty

EN

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 pseudocolour 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.

Słowa kluczowe

EN

CEEMDAN; fault diagnosis; lightweight neural network; marine centrifugal fan

• Wydawca: Gdańsk University of Technology, Faculty of Ocean Engineering & Ship Technology
• Czasopismo: Polish Maritime Research
• Rocznik: 2023
• Tom: nr 1
• Strony: 112--120
• Opis fizyczny: Bibliogr. 20 poz., rys., tab.

Twórcy

autor

Li Congyue

• Shanghai Maritime University, College of Merchant Marine, Shanghai, China

• https://orcid.org/0000-0002-9290-5119

autor

Hu Yihuai

• Shanghai Maritime University, College of Merchant Marine, Shanghai, China

• https://orcid.org/0000-0003-3000-7855

autor

Jiang Jiawei

• Shanghai Institute of Electronic Information Technology, School of Mechanical and Energy Engineering, Shanghai, China

• https://orcid.org/0000-0001-9470-935X

autor

Yan Guohua

• Shanghai Maritime University, College of Merchant Marine, Shanghai, China

• https://orcid.org/0000-0002-1065-5452

Bibliografia

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Uwagi

Opracowanie rekordu ze środków MNiSW, umowa nr SONP/SP/546092/2022 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2024).