Fault diagnosis of bearings based on SSWT, bayes optimisation and CNN

• Autorzy: Yan Guohua , Hu Yihuai , Shi Qingguo

Identyfikatory

DOI

10.2478/pomr-2023-0046

• Języki publikacji: EN

Abstrakty

EN

Bearings are important components of rotating machinery and transmission systems, and are often damaged by wear, overload and shocks. Due to the low resolution of traditional time-frequency analysis for the diagnosis of bearing faults, a synchrosqueezed wavelet transform (SSWT) is proposed to improve the resolution. An improved convolutional neural network fault diagnosis model is proposed in this paper, and a Bayesian optimisation method is applied to automatically adjust the structure and hyperparameters of the model to improve the accuracy of bearing fault diagnosis. Experimental results from the accelerated life testing of bearings show that the proposed method is able to accurately identify various types of bearing fault and the different status of these faults under complex running conditions, while achieving very good generalisation ability.

Słowa kluczowe

EN

fault diagnosis; bearing; PMSM; bayesian optimisation; CNN

• Wydawca: Gdańsk University of Technology, Faculty of Ocean Engineering & Ship Technology
• Czasopismo: Polish Maritime Research
• Rocznik: 2023
• Tom: nr 3
• Strony: 132--141
• Opis fizyczny: Bibliogr. 25 poz., rys., tab.

Twórcy

autor

Yan Guohua

• Merchant Marine College, Shanghai Maritime University, China

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

autor

Hu Yihuai

• Merchant Marine College, Shanghai Maritime University, China

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

autor

Shi Qingguo

• Merchant Marine College, Shanghai Maritime University, China

• https://orcid.org/0000-0003-3776-8438

Bibliografia

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Uwagi

PL

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