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| Fault Diagnosis Method of Paper Machine Bearings for Variable Working Conditions Based on Parallel Heterogeneous Network in Small Samples |
| Received:May 08, 2024 Revised:May 30, 2024 |
| DOI：10.11981/j.issn.1000-6842.2025.01.179 |
| Key Words:parallel heterogeneous CNN;paper machine bearings;bearing fault diagnosis |
| Fund Project:国家自然科学基金（62073206）；陕西省技术创新引导专项（2023GXLH-071）。 |
| Author Name | Affiliation | Postcode | | TANG Wei | School of Electrical and Control Engineering， Shaanxi University of Science and Technology， Xi’an， Shaanxi Province 710021
Shaanxi Xiwei Process Automation Engineering Co.， Ltd.， Xianyang， Shaanxi Province， 712000 | 712000 | | YANG Yijun* | School of Electrical and Control Engineering， Shaanxi University of Science and Technology， Xi’an， Shaanxi Province 710021 | 710021 | | WANG Mengxiao | Shaanxi Xiwei Process Automation Engineering Co.， Ltd.， Xianyang， Shaanxi Province， 712000 | 712000 | | LIU Yingwei | School of Electrical and Control Engineering， Shaanxi University of Science and Technology， Xi’an， Shaanxi Province 710021 | 710021 |
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| Abstract: |
| In practical applications， the traditional paper machine bearing fault diagnosis model has problems， such as decreased accuracy in fault diagnosis under variable working conditions due to the small amount of fault vibration signal data and low proportion of effective signal information. In response to this issue， this project proposed a fault diagnosis method of paper machine bearings for variable working conditions based on parallel heterogeneous network in small samples. Firstly， the source and target domain signals were converted into corresponding Gram angle field matrix， Markov transition field matrix， and Euclidean distance matrix， respectively. The obtained three matrices were cross combined row by row and used as network inputs. Secondly， based on convolutional neural network （CNN）， 2D-CNN was improved by designing a multi-channel parallel heterogeneous network that integrated attention mechanism to automatically extract deep features of signals. Then， based on adversarial thinking， domain discriminators and classifiers were designed to align the feature edge distributions of the two domains through multi kernel maximum mean difference （MK-MMD）， achieving recognition of bearing faults under variable operating conditions. Finally， transfer learning experiments were conducted to verify the data collected from the Case Western Reserve University rolling bearing dataset， and the laboratory self-built paper machine bearing fault simulation platform. The results indicated that the paper machine bearing fault transfer learning network model had excellent feature mining ability and high recognition accuracy for paper machine bearing faults under variable operating conditions. |
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