Research on Vibration Measurement and Analysis Technology of Circuit Breaker Based on VMD and LSTM
Source abstract
Abstract
In this paper, we propose a mechanical fault diagnosis technology for circuit breakers based on the NGO-VMD, aiming to improve the accuracy and efficiency of fault diagnosis. The circuit breaker is a key protection device in power systems, and its operational status is crucial to grid security. This paper introduces the NGO-VMD method to decompose its vibration signals, aiming to improve the accuracy and efficiency of fault diagnosis. Failure to detect and address mechanical faults in circuit breakers can lead to equipment damage, power outages, and even personal injury. Therefore, it is of great significance to develop efficient and accurate mechanical fault diagnosis technology for after converting the mechanical fault signal of the vacuum circuit breaker in the distribution network into the IMF form, the modal information of the vibration signal under different faults of the circuit breaker is effectively extracted, and the singular value decomposition of the IMF signal component is carried out to make the information characteristics contained more obvious, Finally, LSTM is used to achieve precise identification of circuit breaker faults. In this paper, the experimental test is carried out on the basis of the actual vacuum circuit breaker in the distribution network, and the feasibility of the design scheme is verified by comprehensive analysis. The comparison and analysis with other methods can be obtained, and the scheme has the advantages of higher efficiency, stronger stability and more accuracy.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Power System Reliability and Maintenance · Thermal Analysis in Power Transmission · Vacuum and Plasma Arcs
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
School of Electronic Information, Xi’an Polytechnic University, Xi’an 710048, China
References (23)
Show source-provided references
1. Methodology to incorporate stuck condition of the circuit breaker in reliability evaluation of electrical distribution networks10.1049/iet-gtd.2020.0164 ↗
2. Mechanical Condition Recognition of Medium-Voltage Vacuum Circuit Breaker Based on Mechanism Dynamic Features Simulation and ANN10.1109/TPWRD.2005.848462 ↗
3. Intelligent Failure Diagnosis for Gas Circuit Breakers Based on Dynamic Resistance Measurements
4. The impact of high-voltage circuit breaker condition on power system reliability indices10.1049/gtd2.13333 ↗
5. On-line hybrid fault diagnosis method for high voltage circuit breaker
6. Li, L., Wang, B., Liu, Y., Yu, H., Zhang, S., and Huang, M. (2023). Early Detection of Health Condition Degradation of Circuit Breaker Based on Electrical Quantity Monitoring. Energies, 16.10.3390/en16145581 ↗
7. Li, X., Shi, D., and Yu, Z. (2020). Nondestructive Damage Testing of Beam Structure Based on Vibration Response Signal Analysis. Materials, 13.10.3390/ma13153301 ↗
8. Cao, S., Zhao, T., Wang, G., Zhang, T., Liu, C., Liu, Q., Zhang, Z., and Wang, X. (2023). A Mechanical Defect Localization and Identification Method for High-Voltage Circuit Breakers Based on the Segmentation of Vibration Signals and Extraction of Chaotic Features. Sensors, 23.10.3390/s23167201 ↗
9. A New Vibration Analysis Approach for Detecting Mechanical Anomalies on Power Circuit Breakers10.1109/ACCESS.2019.2893922 ↗
10. Dou, L., Wan, S., and Zhan, C. (2018). Application of Multiscale Entropy in Mechanical Fault Diagnosis of High Voltage Circuit Breaker. Entropy, 20.10.3390/e20050325 ↗
11. Vibration Signal Augmentation Method for Fault Diagnosis of Low-Voltage Circuit Breaker Based on W-CGAN
12. Qi, J., Gao, X., and Huang, N. (2020). Mechanical Fault Diagnosis of a High Voltage Circuit Breaker Based on High-Efficiency Time-Domain Feature Extraction with Entropy Features. Entropy, 22.10.3390/e22040478 ↗
13. Remaining useful life prognosis of vacuum circuit breaker mechanical performance via vibration signal feature fusion10.1088/1361-6501/adec0c ↗
14. Jiang, P., Li, H., Yan, X., Zhang, L., and Li, W. (2023). Identification of NOL-Ring Composite Materials’ Damage Mechanism Based on the STOA-VMD Algorithm. Polymers, 15.10.3390/polym15122647 ↗
15. Wang, Y., Zhang, S., Cao, R., Xu, D., and Fan, Y. (2023). A Rolling Bearing Fault Diagnosis Method Based on the WOA-VMD and the GAT. Entropy, 25.10.3390/e25060889 ↗
16. Zhong, J., Chen, T., and Yi, L. (2023). Face expression recognition based on NGO-BILSTM model. Front. Neurorobotics, 17.10.3389/fnbot.2023.1155038 ↗
17. Yang, J. (2024). Research on Feature Extraction and Fault Diagnosis Method for Rolling Bearing Vibration Signals Based on Improved FDM-SVD and CYCBD. Symmetry, 16.10.3390/sym16050552 ↗
18. Zhang, P., Li, S., Xu, K., Gong, S., Zhan, K., Shen, M., Tian, J., and Yuan, X. (2022). A Novel Strategy for Signal Denoising Using Two-Layer SVD and Its Application to Rub-Impact Fault Diagnosis of Aeroengine. Appl. Sci., 12.10.3390/app12052342 ↗
19. Application of Improved VMD-LSTM Model in Sports Artificial Intelligence
20. A Novel Approach of Wind Turbine Blade Root Load Monitoring Based on Bearings Vibrational Data and a New AE-LSTM-Attention Neural Network10.1002/we.70006 ↗
21. Li, K., Zhen, Y., Li, P., Hu, X., and Yang, L. (2025). Optical Fiber Vibration Signal Recognition Based on the EMD Algorithm and CNN-LSTM. Sensors, 25.10.3390/s25072016 ↗
22. A hybrid LSTM random forest model with grey wolf optimization for enhanced detection of multiple bearing faults10.1038/s41598-024-75174-x ↗
23. Northern goshawk optimization: A new swarm-based algorithm for solving optimization problems10.1109/ACCESS.2021.3133286 ↗