Journal
ISA TRANSACTIONS
Volume 128, Issue -, Pages 550-564Publisher
ELSEVIER SCIENCE INC
DOI: 10.1016/j.isatra.2021.11.020
Keywords
Bearing fault diagnosis; Multi-sensor data fusion; Inception network; Attention mechanism
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Funding
- National Key Research and Development Program of China [2018YFB2000504]
- Key projects of Natural Science Basic Research Plan in Shaanxi Province of China [2021JZ-02]
- Fundamental Research Funds for the Central Universities
- National Science, China [xzd012019032]
- Open fund funded project of Henan Key Laboratory of high-performance bearing technology, China [2020ZCKF04]
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Methods with multi-sensor data fusion improve the accuracy and robustness of bearing fault diagnosis. This paper proposes a novel model of multi-layer deep fusion network with attention mechanism (AMMFN) to enhance the information interaction and achieve adaptive hierarchical fusion. Extensive experiments demonstrate its higher accuracy and stronger generalization ability compared to other methods.
The methods with multi-sensor data fusion have been a remarkable way to improve the accuracy and robustness of bearing fault diagnosis under complicated conditions. However, most of the existing fusion models or methods belong to single fusion level and simple fusion structure is usually utilized, and the correlation and complementarity of information between multi-sensor data might be easily ignored. In order to improve the performance of fault diagnosis with multi-sensor data fusion, this paper proposes a novel model of multi-layer deep fusion network with attention mechanism (AMMFN). The proposed model consists of a central network and multiple branch networks stacking by Inception networks, and the deep features of each single-sensor data are extracted automatically by the branch networks, and the extracted features of multi-sensor data at different levels are fused with the central network, and then the information interaction between multi-sensor data can be significantly enhanced and the adaptive hierarchical fusion of information can be achieved. Moreover, a fusion strategy based on attention mechanism is designed to extract more correlation information during the fusion of features extracted from multi-sensor data. Extensive experiments are also performed to evaluate the performance of proposed approach, and the comparison results with other methods indicate that the presented method takes higher accuracy and stronger generalization ability. (c) 2021 ISA. Published by Elsevier Ltd. All rights reserved.
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