Research on Fault Diagnosis of Highway Bi-LSTM Based on Attention Mechanism

• Autorzy: Li Xueyi , Su Kaiyu , He Qiushi , Wang Xiangkai , Xie Zhijie

Identyfikatory

DOI

10.17531/ein/162937

• Języki publikacji: EN

Abstrakty

EN

Deep groove ball bearings are widely used in rotary machinery. Accurate for bearing faults diagnosis is essential for equipment maintenance. For common depth learning methods, the feature extraction of inverse time domain signal direction and the attention to key features are usually ignored. Based on the long short term memory(LSTM) network, this study proposes an attention-based highway bidirectional long short term memory (AHBi-LSTM) network for fault diagnosis based on the raw vibration signal. By increasing the Attention mechanism and Highway, the ability of the network to extract features is increased. The bidirectional LSTM network simultaneously extracts the raw vibration signal in positive and inverse time-domains to better extract the fault features. Six deep groove ball bearings with different health conditions were used to validate the AHBi-LSTM method in an experiment. The results showed that the accuracy of the proposed method for bearing fault diagnosis was over 98%, which was 8.66% higher than that of the LSTM model. The AHBi-LSTM model is also better than other relevant models for bearing fault diagnosis.

Słowa kluczowe

EN

fault diagnosis; Bi-LSTM; attention; highway; deep learning; Ball Bearing

• Wydawca: Polskie Naukowo-Techniczne Towarzystwo Eksploatacyjne
• Czasopismo: Eksploatacja i Niezawodność
• Rocznik: 2023
• Tom: Vol. 25, no. 2
• Strony: art. no. 162937
• Opis fizyczny: Bibliogr. 35 poz., rys., tab., wykr.

Twórcy

autor

Li Xueyi

• College of Mechanical and Electrical Engineering, Northeast Forestry University, Harbin, 150040, China

• https://orcid.org/0000-0003-0335-0594

autor

Su Kaiyu

• College of Mechanical and Electrical Engineering, Northeast Forestry University, Harbin, 150040, China

• https://orcid.org/0000-0001-9442-4071

autor

He Qiushi

• College of Mechanical and Electrical Engineering, Northeast Forestry University, Harbin, 150040, China

• https://orcid.org/0000-0002-3998-7039

autor

Wang Xiangkai

• College of Mechanical and Electrical Engineering, Northeast Forestry University, Harbin, 150040, China

• https://orcid.org/0000-0002-0037-3259

autor

Xie Zhijie

• College of Mechanical and Electrical Engineering, Northeast Forestry University, Harbin, 150040, China

• https://orcid.org/0000-0002-1104-2375

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