An Intelligent Fault Diagnosis Model for Rolling Bearings Based on IGTO-Optimized VMD and LSTM Networks
To address the issue of rolling bearing fault diagnosis, this paper proposes a novel model combining the Improved Gorilla Troop Optimization (IGTO) algorithm, Variational Mode Decomposition (VMD), Permutation Entropy (PE), and Long Short-Term Memory (LSTM) networks. The IGTO algorithm is used to opt...
Saved in:
| Main Authors: | , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2025-04-01
|
| Series: | Applied Sciences |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2076-3417/15/8/4338 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1850144588697698304 |
|---|---|
| author | Xianglong Luo Fengrong Yu Jing Qian Biao An Nengpeng Duan |
| author_facet | Xianglong Luo Fengrong Yu Jing Qian Biao An Nengpeng Duan |
| author_sort | Xianglong Luo |
| collection | DOAJ |
| description | To address the issue of rolling bearing fault diagnosis, this paper proposes a novel model combining the Improved Gorilla Troop Optimization (IGTO) algorithm, Variational Mode Decomposition (VMD), Permutation Entropy (PE), and Long Short-Term Memory (LSTM) networks. The IGTO algorithm is used to optimize the parameters of VMD and LSTM, enhancing signal decomposition and feature extraction. The proposed model achieves fault classification accuracies of 96.67% and 98.96% in the testing and training phases, respectively, on the Case Western Reserve University dataset, with minimal accuracy fluctuations. Furthermore, on the Jiangnan University dataset, the model reaches an average testing accuracy of 98.85%, with the highest accuracy reaching 99.48%. The results also demonstrate high stability, as indicated by low standard deviations (1.2148 and 1.3217) and narrow 95% confidence intervals ([95.75%, 97.58%] and [96.73%, 97.49%]). Despite a longer average runtime of 13.88 s per sample, the model’s superior accuracy justifies the computational cost. These results demonstrate the model’s excellent diagnostic performance, adaptability to different datasets, and practical applicability for rolling bearing fault diagnosis. This approach provides a valuable reference for predictive maintenance and fault detection systems in industrial applications. |
| format | Article |
| id | doaj-art-b173316fc9dc43b49b298df4a238aee6 |
| institution | OA Journals |
| issn | 2076-3417 |
| language | English |
| publishDate | 2025-04-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj-art-b173316fc9dc43b49b298df4a238aee62025-08-20T02:28:19ZengMDPI AGApplied Sciences2076-34172025-04-01158433810.3390/app15084338An Intelligent Fault Diagnosis Model for Rolling Bearings Based on IGTO-Optimized VMD and LSTM NetworksXianglong Luo0Fengrong Yu1Jing Qian2Biao An3Nengpeng Duan4School of Metallurgy and Energy Engineering, Kunming University of Science and Technology, Kunming 650093, ChinaSchool of Metallurgy and Energy Engineering, Kunming University of Science and Technology, Kunming 650093, ChinaSchool of Metallurgy and Energy Engineering, Kunming University of Science and Technology, Kunming 650093, ChinaSchool of Metallurgy and Energy Engineering, Kunming University of Science and Technology, Kunming 650093, ChinaSchool of Metallurgy and Energy Engineering, Kunming University of Science and Technology, Kunming 650093, ChinaTo address the issue of rolling bearing fault diagnosis, this paper proposes a novel model combining the Improved Gorilla Troop Optimization (IGTO) algorithm, Variational Mode Decomposition (VMD), Permutation Entropy (PE), and Long Short-Term Memory (LSTM) networks. The IGTO algorithm is used to optimize the parameters of VMD and LSTM, enhancing signal decomposition and feature extraction. The proposed model achieves fault classification accuracies of 96.67% and 98.96% in the testing and training phases, respectively, on the Case Western Reserve University dataset, with minimal accuracy fluctuations. Furthermore, on the Jiangnan University dataset, the model reaches an average testing accuracy of 98.85%, with the highest accuracy reaching 99.48%. The results also demonstrate high stability, as indicated by low standard deviations (1.2148 and 1.3217) and narrow 95% confidence intervals ([95.75%, 97.58%] and [96.73%, 97.49%]). Despite a longer average runtime of 13.88 s per sample, the model’s superior accuracy justifies the computational cost. These results demonstrate the model’s excellent diagnostic performance, adaptability to different datasets, and practical applicability for rolling bearing fault diagnosis. This approach provides a valuable reference for predictive maintenance and fault detection systems in industrial applications.https://www.mdpi.com/2076-3417/15/8/4338rolling bearingfault diagnosisimproved gorilla troops optimizationvariational mode decompositionlong-term memory network |
| spellingShingle | Xianglong Luo Fengrong Yu Jing Qian Biao An Nengpeng Duan An Intelligent Fault Diagnosis Model for Rolling Bearings Based on IGTO-Optimized VMD and LSTM Networks Applied Sciences rolling bearing fault diagnosis improved gorilla troops optimization variational mode decomposition long-term memory network |
| title | An Intelligent Fault Diagnosis Model for Rolling Bearings Based on IGTO-Optimized VMD and LSTM Networks |
| title_full | An Intelligent Fault Diagnosis Model for Rolling Bearings Based on IGTO-Optimized VMD and LSTM Networks |
| title_fullStr | An Intelligent Fault Diagnosis Model for Rolling Bearings Based on IGTO-Optimized VMD and LSTM Networks |
| title_full_unstemmed | An Intelligent Fault Diagnosis Model for Rolling Bearings Based on IGTO-Optimized VMD and LSTM Networks |
| title_short | An Intelligent Fault Diagnosis Model for Rolling Bearings Based on IGTO-Optimized VMD and LSTM Networks |
| title_sort | intelligent fault diagnosis model for rolling bearings based on igto optimized vmd and lstm networks |
| topic | rolling bearing fault diagnosis improved gorilla troops optimization variational mode decomposition long-term memory network |
| url | https://www.mdpi.com/2076-3417/15/8/4338 |
| work_keys_str_mv | AT xianglongluo anintelligentfaultdiagnosismodelforrollingbearingsbasedonigtooptimizedvmdandlstmnetworks AT fengrongyu anintelligentfaultdiagnosismodelforrollingbearingsbasedonigtooptimizedvmdandlstmnetworks AT jingqian anintelligentfaultdiagnosismodelforrollingbearingsbasedonigtooptimizedvmdandlstmnetworks AT biaoan anintelligentfaultdiagnosismodelforrollingbearingsbasedonigtooptimizedvmdandlstmnetworks AT nengpengduan anintelligentfaultdiagnosismodelforrollingbearingsbasedonigtooptimizedvmdandlstmnetworks AT xianglongluo intelligentfaultdiagnosismodelforrollingbearingsbasedonigtooptimizedvmdandlstmnetworks AT fengrongyu intelligentfaultdiagnosismodelforrollingbearingsbasedonigtooptimizedvmdandlstmnetworks AT jingqian intelligentfaultdiagnosismodelforrollingbearingsbasedonigtooptimizedvmdandlstmnetworks AT biaoan intelligentfaultdiagnosismodelforrollingbearingsbasedonigtooptimizedvmdandlstmnetworks AT nengpengduan intelligentfaultdiagnosismodelforrollingbearingsbasedonigtooptimizedvmdandlstmnetworks |