Search for Author, Title, Keyword
Research on Fault Diagnosis of Highway Bi-LSTM Based on Attention Mechanism
More details
Hide details
College of Mechanical and Electrical Engineering, Northeast Forestry University, Harbin, 150040, China.
Online publication date: 2023-04-04
Publication date: 2023-04-04
Eksploatacja i Niezawodność – Maintenance and Reliability 2023;25(2):162937
  • The AHBi-LSTM method proposed can simultaneously process the bearing raw vibration signals according to the positive and inverse time-domain sequences, which is more conducive to practical industrial applications.
  • The Attention mechanism allows the network to pay attention to essential features in different time steps, improving the fault diagnosis accuracy for deep groove ball bearing.
  • The AHBi-LSTM method introduces an adaptive gating mechanism to manage the information flow in the network. The method can effectively solve multi-layer networks that are difficult to train.
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.
This work is supported in part by the Fundamental Research Funds for the Central Universities(No.2572022BF07) and in part by the Key Laboratory of Vibration and Control of Aero-Propulsion System, Ministry of Education, Northeastern University (VCAME202209).
A Migration Learning Method Based on Adaptive Batch Normalization Improved Rotating Machinery Fault Diagnosis
Xueyi Li, Tianyu Yu, Daiyou Li, Xiangkai Wang, Cheng Shi, Zhijie Xie, Xiangwei Kong