Time-Frequency Fusion Features-Based GSWOA-KELM Model for Gear Fault Diagnosis
To improve the accuracy of gear fault diagnosis and overcome the low diagnostic accuracy of the model caused by manual parameter selection, a combined diagnostic model based on time-frequency fusion features is combined with the improved global search whale optimization algorithm (GSWOA) to optimize...
| Main Authors: | , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2023-12-01
|
| Series: | Lubricants |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2075-4442/12/1/10 |
| _version_ | 1797339634242420736 |
|---|---|
| author | Qin Hu Haiting Zhou Chengcheng Wang Chenxi Zhu Jiaping Shen Peng He |
| author_facet | Qin Hu Haiting Zhou Chengcheng Wang Chenxi Zhu Jiaping Shen Peng He |
| author_sort | Qin Hu |
| collection | DOAJ |
| description | To improve the accuracy of gear fault diagnosis and overcome the low diagnostic accuracy of the model caused by manual parameter selection, a combined diagnostic model based on time-frequency fusion features is combined with the improved global search whale optimization algorithm (GSWOA) to optimize the fault diagnosis capability of the kernel extreme learning machine (KELM). First, the time-domain and frequency-domain features of the gear fault state are extracted separately, and feature vectors are constructed through feature fusion, which overcomes the limitations of single features. Second, the GSWOA based on three strategies is used to optimize the regularization coefficient C and kernel function parameter γ of KELM, and a GSWOA-KELM fault diagnosis model is built to avoid the problem of low fault diagnosis accuracy caused by the manual selection of KELM parameters. Finally, the public dataset from Southeast University is taken to verify the performance of the proposed model by comparing it with KELM, SSA-KELM, and WOA-KELM models. The experimental results demonstrate that the improved time-frequency fusion features-based GSWOA-KELM model shows faster convergence speed and stronger global search ability. Compared with KELM, SSA-KELM, and WOA-KELM models, the performance of the proposed model has been improved by 11.33%, 8.67%, and 1.33%, respectively. |
| first_indexed | 2024-03-08T09:51:10Z |
| format | Article |
| id | doaj.art-ad48727bc814417d83396014834a7486 |
| institution | Directory Open Access Journal |
| issn | 2075-4442 |
| language | English |
| last_indexed | 2024-03-08T09:51:10Z |
| publishDate | 2023-12-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Lubricants |
| spelling | doaj.art-ad48727bc814417d83396014834a74862024-01-29T14:04:14ZengMDPI AGLubricants2075-44422023-12-011211010.3390/lubricants12010010Time-Frequency Fusion Features-Based GSWOA-KELM Model for Gear Fault DiagnosisQin Hu0Haiting Zhou1Chengcheng Wang2Chenxi Zhu3Jiaping Shen4Peng He5School of Quality and Safety Engineering, China Jiliang University, Hangzhou 310018, ChinaSchool of Quality and Safety Engineering, China Jiliang University, Hangzhou 310018, ChinaInstrumental Technol & Econ Inst, Beijing 100032, ChinaSchool of Quality and Safety Engineering, China Jiliang University, Hangzhou 310018, ChinaSchool of Quality and Safety Engineering, China Jiliang University, Hangzhou 310018, ChinaSchool of Quality and Safety Engineering, China Jiliang University, Hangzhou 310018, ChinaTo improve the accuracy of gear fault diagnosis and overcome the low diagnostic accuracy of the model caused by manual parameter selection, a combined diagnostic model based on time-frequency fusion features is combined with the improved global search whale optimization algorithm (GSWOA) to optimize the fault diagnosis capability of the kernel extreme learning machine (KELM). First, the time-domain and frequency-domain features of the gear fault state are extracted separately, and feature vectors are constructed through feature fusion, which overcomes the limitations of single features. Second, the GSWOA based on three strategies is used to optimize the regularization coefficient C and kernel function parameter γ of KELM, and a GSWOA-KELM fault diagnosis model is built to avoid the problem of low fault diagnosis accuracy caused by the manual selection of KELM parameters. Finally, the public dataset from Southeast University is taken to verify the performance of the proposed model by comparing it with KELM, SSA-KELM, and WOA-KELM models. The experimental results demonstrate that the improved time-frequency fusion features-based GSWOA-KELM model shows faster convergence speed and stronger global search ability. Compared with KELM, SSA-KELM, and WOA-KELM models, the performance of the proposed model has been improved by 11.33%, 8.67%, and 1.33%, respectively.https://www.mdpi.com/2075-4442/12/1/10gear fault diagnosiskernel extreme learning machineglobal search whale optimization algorithmfeature fusionmachine learning |
| spellingShingle | Qin Hu Haiting Zhou Chengcheng Wang Chenxi Zhu Jiaping Shen Peng He Time-Frequency Fusion Features-Based GSWOA-KELM Model for Gear Fault Diagnosis Lubricants gear fault diagnosis kernel extreme learning machine global search whale optimization algorithm feature fusion machine learning |
| title | Time-Frequency Fusion Features-Based GSWOA-KELM Model for Gear Fault Diagnosis |
| title_full | Time-Frequency Fusion Features-Based GSWOA-KELM Model for Gear Fault Diagnosis |
| title_fullStr | Time-Frequency Fusion Features-Based GSWOA-KELM Model for Gear Fault Diagnosis |
| title_full_unstemmed | Time-Frequency Fusion Features-Based GSWOA-KELM Model for Gear Fault Diagnosis |
| title_short | Time-Frequency Fusion Features-Based GSWOA-KELM Model for Gear Fault Diagnosis |
| title_sort | time frequency fusion features based gswoa kelm model for gear fault diagnosis |
| topic | gear fault diagnosis kernel extreme learning machine global search whale optimization algorithm feature fusion machine learning |
| url | https://www.mdpi.com/2075-4442/12/1/10 |
| work_keys_str_mv | AT qinhu timefrequencyfusionfeaturesbasedgswoakelmmodelforgearfaultdiagnosis AT haitingzhou timefrequencyfusionfeaturesbasedgswoakelmmodelforgearfaultdiagnosis AT chengchengwang timefrequencyfusionfeaturesbasedgswoakelmmodelforgearfaultdiagnosis AT chenxizhu timefrequencyfusionfeaturesbasedgswoakelmmodelforgearfaultdiagnosis AT jiapingshen timefrequencyfusionfeaturesbasedgswoakelmmodelforgearfaultdiagnosis AT penghe timefrequencyfusionfeaturesbasedgswoakelmmodelforgearfaultdiagnosis |