Fault diagnosis of shearer cutting unit gearbox based on improved cascaded broad learning
The vibration monitoring data of the shearer cutting unit gearbox has a complex structure and is prone to class imbalance issues, leading to frequent false positives in traditional machine learning-based fault diagnosis methods. Meanwhile, deep learning-based approaches often suffer from complex mod...
Saved in:
| Main Authors: | , , , , , |
|---|---|
| Format: | Article |
| Language: | zho |
| Published: |
Editorial Department of Industry and Mine Automation
2025-03-01
|
| Series: | Gong-kuang zidonghua |
| Subjects: | |
| Online Access: | http://www.gkzdh.cn/article/doi/10.13272/j.issn.1671-251x.2024110057 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1849311193391431680 |
|---|---|
| author | LI Xin LI Shuhua CHEN Hao SI Lei WEI Dong ZOU Xiaoyu |
| author_facet | LI Xin LI Shuhua CHEN Hao SI Lei WEI Dong ZOU Xiaoyu |
| author_sort | LI Xin |
| collection | DOAJ |
| description | The vibration monitoring data of the shearer cutting unit gearbox has a complex structure and is prone to class imbalance issues, leading to frequent false positives in traditional machine learning-based fault diagnosis methods. Meanwhile, deep learning-based approaches often suffer from complex model structures, low learning efficiency, and susceptibility to local optima, negatively impacting diagnostic performance. To address these issues, a fault diagnosis method was proposed for the shearer cutting unit gearbox based on improved cascaded broad learning (ICBL). A random hypergraph convolution mechanism was introduced into the feature nodes of the ICBL model to fully exploit the complex multivariate structural information in the vibration data of the shearer cutting unit gearbox, thereby enhancing the representation of fault features. Additionally, a class-specific weight allocation strategy was adopted to assign higher weights to minority class samples based on the class distribution of the input data, improving fault diagnosis performance under imbalanced data conditions. The effectiveness of the ICBL-based fault diagnosis method was validated using a shearer cutting unit gearbox fault simulation test platform. Experimental results demonstrated that the proposed method effectively enhanced the discriminability of fault features, achieving a diagnostic accuracy of 94.52% when the data imbalance ratio was 15, with a fault recognition time of 0.284 ms per sample. The method outperformed cascaded broad learning systems, weighted broad learning systems, multi-scale convolutional neural networks, hypergraph neural networks, and multi-resolution hypergraph convolutional networks, demonstrating significant engineering application value. |
| format | Article |
| id | doaj-art-bccc4407e666476cb397e30ac3e031d9 |
| institution | Kabale University |
| issn | 1671-251X |
| language | zho |
| publishDate | 2025-03-01 |
| publisher | Editorial Department of Industry and Mine Automation |
| record_format | Article |
| series | Gong-kuang zidonghua |
| spelling | doaj-art-bccc4407e666476cb397e30ac3e031d92025-08-20T03:53:28ZzhoEditorial Department of Industry and Mine AutomationGong-kuang zidonghua1671-251X2025-03-01513869510.13272/j.issn.1671-251x.2024110057Fault diagnosis of shearer cutting unit gearbox based on improved cascaded broad learningLI Xin0LI Shuhua1CHEN Hao2SI Lei3WEI Dong4ZOU XiaoyuSchool of Mechanical and Electrical Engineering, China University of Mining and Technology, Xuzhou 221116, ChinaSchool of Mechanical and Electrical Engineering, China University of Mining and Technology, Xuzhou 221116, ChinaSchool of Mechanical and Electrical Engineering, China University of Mining and Technology, Xuzhou 221116, ChinaSchool of Mechanical and Electrical Engineering, China University of Mining and Technology, Xuzhou 221116, ChinaSchool of Mechanical and Electrical Engineering, China University of Mining and Technology, Xuzhou 221116, ChinaThe vibration monitoring data of the shearer cutting unit gearbox has a complex structure and is prone to class imbalance issues, leading to frequent false positives in traditional machine learning-based fault diagnosis methods. Meanwhile, deep learning-based approaches often suffer from complex model structures, low learning efficiency, and susceptibility to local optima, negatively impacting diagnostic performance. To address these issues, a fault diagnosis method was proposed for the shearer cutting unit gearbox based on improved cascaded broad learning (ICBL). A random hypergraph convolution mechanism was introduced into the feature nodes of the ICBL model to fully exploit the complex multivariate structural information in the vibration data of the shearer cutting unit gearbox, thereby enhancing the representation of fault features. Additionally, a class-specific weight allocation strategy was adopted to assign higher weights to minority class samples based on the class distribution of the input data, improving fault diagnosis performance under imbalanced data conditions. The effectiveness of the ICBL-based fault diagnosis method was validated using a shearer cutting unit gearbox fault simulation test platform. Experimental results demonstrated that the proposed method effectively enhanced the discriminability of fault features, achieving a diagnostic accuracy of 94.52% when the data imbalance ratio was 15, with a fault recognition time of 0.284 ms per sample. The method outperformed cascaded broad learning systems, weighted broad learning systems, multi-scale convolutional neural networks, hypergraph neural networks, and multi-resolution hypergraph convolutional networks, demonstrating significant engineering application value.http://www.gkzdh.cn/article/doi/10.13272/j.issn.1671-251x.2024110057shearer cutting unitgearboxfault diagnosiscascaded broad learningrandom hypergraph convolutionclass-specific weight |
| spellingShingle | LI Xin LI Shuhua CHEN Hao SI Lei WEI Dong ZOU Xiaoyu Fault diagnosis of shearer cutting unit gearbox based on improved cascaded broad learning Gong-kuang zidonghua shearer cutting unit gearbox fault diagnosis cascaded broad learning random hypergraph convolution class-specific weight |
| title | Fault diagnosis of shearer cutting unit gearbox based on improved cascaded broad learning |
| title_full | Fault diagnosis of shearer cutting unit gearbox based on improved cascaded broad learning |
| title_fullStr | Fault diagnosis of shearer cutting unit gearbox based on improved cascaded broad learning |
| title_full_unstemmed | Fault diagnosis of shearer cutting unit gearbox based on improved cascaded broad learning |
| title_short | Fault diagnosis of shearer cutting unit gearbox based on improved cascaded broad learning |
| title_sort | fault diagnosis of shearer cutting unit gearbox based on improved cascaded broad learning |
| topic | shearer cutting unit gearbox fault diagnosis cascaded broad learning random hypergraph convolution class-specific weight |
| url | http://www.gkzdh.cn/article/doi/10.13272/j.issn.1671-251x.2024110057 |
| work_keys_str_mv | AT lixin faultdiagnosisofshearercuttingunitgearboxbasedonimprovedcascadedbroadlearning AT lishuhua faultdiagnosisofshearercuttingunitgearboxbasedonimprovedcascadedbroadlearning AT chenhao faultdiagnosisofshearercuttingunitgearboxbasedonimprovedcascadedbroadlearning AT silei faultdiagnosisofshearercuttingunitgearboxbasedonimprovedcascadedbroadlearning AT weidong faultdiagnosisofshearercuttingunitgearboxbasedonimprovedcascadedbroadlearning AT zouxiaoyu faultdiagnosisofshearercuttingunitgearboxbasedonimprovedcascadedbroadlearning |