RESEARCH PAPER
Intelligent Fault Detection and Diagnosis Algorithm of Electrical Equipment Based on Artificial Intelligence Model
1
Hubei University of Automotive Industry, China
2
South China University of Technology, China
Submission date: 2025-05-09
Final revision date: 2025-06-16
Acceptance date: 2025-09-07
Online publication date: 2025-09-14
Publication date: 2025-09-14
Eksploatacja i Niezawodność – Maintenance and Reliability 2026;28(1):210409
HIGHLIGHTS
- This study focuses on the design and application of intelligent fault detection.
- This study aims to improve the accuracy of electrical equipment fault detection.
- The study explores influence of different noise levels on the performance of the model.
KEYWORDS
TOPICS
ABSTRACT
In the context of digital transformation, it is essential to ensure the safe operation of electrical equipment. In order to solve the problem of low accuracy of existing electrical equipment fault detection algorithms in diagnosing unknown faults, this study collects industrial field data to construct a dataset, and develops a fault identification model integrating convolutional neural network and long short-term memory network based on deep learning framework. Experiments show that the model has an average accuracy of 98.5% in the detection of five main fault types, which is nearly 10% higher than that of the traditional method, and the recognition rate of subtle faults is over 96%, with good generalization and robustness. The study also analyzes the impact of noise and optimizes the hyperparameters, which is expected to promote the upgrade of intelligent operation and maintenance in the manufacturing industry.
REFERENCES (29)
1.
M. Bjelic, B. Brkovic, M. Zarkovic, and T. Miljkovic, "Machine learning for power transformer SFRA based fault detection," International Journal of Electrical Power & Energy Systems, vol. 156, 2024. https://doi.org/10.1016/j.ijep....
2.
A. Safian, N. Wu, and X. H. Liang, "A multi-function integrated PVDF transducer for fault detection and speed measurement of cylindrical roller bearings," Mechanical Systems and Signal Processing, vol. 212, 2024. https://doi.org/10.1016/j.ymss....
3.
F. Cordoni, G. Bacchiega, G. Bondani, R. Radu, and R. Muradore, "A multi-modal unsupervised fault detection system based on power signals and thermal imaging via deep AutoEncoder neural network," Engineering Applications of Artificial Intelligence, vol. 110, 2022. https://doi.org/10.1016/j.enga....
4.
J. Jenis, J. Ondriga, S. Hrcek, F. Brumercik, M. Cuchor, and E. Sadovsky, "Engineering Applications of Artificial Intelligence in Mechanical Design and Optimization," Machines, vol. 11, no. 6, 2023. https://doi.org/10.3390/machin....
5.
G. Elkhawaga, O. Elzeki, M. Abuelkheir, and M. Reichert, "Evaluating Explainable Artificial Intelligence Methods Based on Feature Elimination: A Functionality-Grounded Approach," Electronics, vol. 12, no. 7, 2023. https://doi.org/10.3390/electr....
6.
M. S. Raunak and R. Kuhn, "Explainable Artificial Intelligence and Machine Learning," Computer, vol. 54, no. 10, pp. 25-27, 2021. https://doi.org/10.1109/MC.202....
7.
M. Mersha, K. Lam, J. Wood, A. K. Alshami, and J. Kalita, "Explainable artificial intelligence: A survey of needs, techniques, applications, and future direction," Neurocomputing, vol. 599, 2024. https://doi.org/10.1016/j.neuc....
8.
P. Sun and L. Gu, "Fuzzy knowledge graph system for artificial intelligence-based smart education," Journal of Intelligent & Fuzzy Systems, vol. 40, no. 2, pp. 2929-2940, 2021. https://doi.org/10.3233/JIFS-1....
9.
S. Selvarajan et al., "Generative artificial intelligence and adversarial network for fraud detections in current evolutional systems," Expert Systems, vol. 2024. https://doi.org/10.1111/exsy.1....
10.
M. Jovanovic and M. Campbell, "Generative Artificial Intelligence: Trends and Prospects," Computer, vol. 55, no. 10, pp. 107-112, 2022. https://doi.org/10.1109/MC.202....
11.
C. Wu, S. Guo, Y. Wu, J. Ai, and N. N. Xiong, "Networked Fault Detection of Field Equipment from Monitoring System Based on Fusing of Motion Sensing and Appearance Information," Multimedia Tools and Applications, vol. 79, no. 23-24, pp. 16319-16348, 2020. https://doi.org/10.1007/s11042....
12.
S. Chen, J. Yu, and S. Wang, "One-dimensional convolutional neural network-based active feature extraction for fault detection and diagnosis of industrial processes and its understanding via visualization," Isa Transactions, vol. 122, pp. 424-443, 2022. https://doi.org/10.1016/j.isat....
13.
Z. Xia, F. Ye, M. Dai, and Z. Zhang, "Real-time fault detection and process control based on multi-channel sensor data fusion," International Journal of Advanced Manufacturing Technology, vol. 115, no. 3, pp. 795-806, 2021. https://doi.org/10.1007/s00170....
14.
C.-F. Chien, W.-T. Hung, and E. T.-Y. Liao, "Redefining Monitoring Rules for Intelligent Fault Detection and Classification via CNN Transfer Learning for Smart Manufacturing," IEEE Transactions on Semiconductor Manufacturing, vol. 35, no. 2, pp. 158-165, 2022. https://doi.org/10.1109/TSM.20....
15.
Q. Y. Lu et al., "Research on fault detection and remote monitoring system of variable speed constant frequency wind turbine based on Internet of Things," Journal of High Speed Networks, vol. 30, no. 2, pp. 175-189, 2024. https://doi.org/10.3233/JHS-22....
16.
M. M. Jaber et al., "Resnet-based deep learning multilayer fault detection model-based fault diagnosis," Multimedia Tools and Applications, vol. 83, no. 7, pp. 19277-19300, 2024. https://doi.org/10.1007/s11042....
17.
C. E. Sunal, V. Dyo, and V. Velisavljevic, "Review of Machine Learning Based Fault Detection for Centrifugal Pump Induction Motors," Ieee Access, vol. 10, pp. 71344-71355, 2022. https://doi.org/10.1109/ACCESS....
18.
L. Guo, H. Shi, S. Tan, B. Song, and Y. Tao, "Sensor Fault Detection and Diagnosis Using Graph Convolutional Network Combining Process Knowledge and Process Data," IEEE Transactions on Instrumentation and Measurement, vol. 72, 2023. https://doi.org/10.1109/TIM.20....
19.
I. Zamudio-Ramirez, R. Alfredo Osornio-Rios, and J. Alfonso Antonino-Daviu, "Smart Sensor for Fault Detection in Induction Motors Based on the Combined Analysis of Stray-Flux and Current Signals: A Flexible, Robust Approach," IEEE Industry Applications Magazine, vol. 28, no. 2, pp. 56-66, 2022. https://doi.org/10.1109/MIAS.2....
20.
M. Lamraoui, "Spindle bearing fault detection in high-speed milling machines in non-stationary conditions," International Journal of Advanced Manufacturing Technology, vol. 124, no. 3-4, pp. 1253-1271, 2023. https://doi.org/10.1007/s00170....
21.
B. U. Deveci, M. Celtikoglu, O. Albayrak, P. Unal, and P. Kirci, "Transfer Learning Enabled Bearing Fault Detection Methods Based on Image Representations of Single-Dimensional Signals," Information Systems Frontiers, vol. 2023. https://doi.org/10.1007/s10796....
22.
H. Qiang, Z. Tao, B. Ye, R. Yang, and W. Xu, "Transmission Line Fault Detection and Classification Based on Improved YOLOv8s," Electronics, vol. 12, no. 21, 2023. https://doi.org/10.3390/electr....
23.
J. Zheng, J. Liao, and Y. Zhu, "Two-Stage Multi-Channel Fault Detection and Remaining Useful Life Prediction Model of Internal Gear Pumps Based on Robust-ResNet," Sensors, vol. 23, no. 5, 2023. https://doi.org/10.3390/s23052....
24.
G. J. Han et al., "Typical Fault Detection on Drone Images of Transmission Lines Based on Lightweight Structure and Feature-Balanced Network," Drones, vol. 7, no. 10, 2023. https://doi.org/10.3390/drones....
25.
L. Zhao, Z. Liu, P. Yuan, G. Wen, and X. Huang, "Vibration feature extraction and fault detection method for transmission towers," IET Science Measurement & Technology, vol. 18, no. 5, pp. 203-218, 2024. https://doi.org/10.1049/smt2.1....
26.
Z. Li, J. Ma, J. Wu, P. K. Wong, X. Wang, and X. Li, "A Gated Recurrent Generative Transfer Learning Network for Fault Diagnostics Considering Imbalanced Data and Variable Working Conditions," IEEE Transactions on Neural Networks and Learning Systems, vol. 2024.
27.
J. Zhao, Y.-G. Li, and S. Sampath, "A hierarchical structure built on physical and data-based information for intelligent aero-engine gas path diagnostics," Applied Energy, vol. 332, 2023. https://doi.org/10.1016/j.apen....
28.
A. Heydari, D. A. Garcia, A. Fekih, F. Keynia, L. B. Tjernberg, and L. De Santoli, "A Hybrid Intelligent Model for the Condition Monitoring and Diagnostics of Wind Turbines Gearbox," IEEE Access, vol. 9, pp. 89878-89890, 2021. https://doi.org/10.1109/ACCESS....
29.
M. Skowron, C. T. Kowalski, and T. Orlowska-Kowalska, "Impact of the Convolutional Neural Network Structure and Training Parameters on the Effectiveness of the Diagnostic Systems of Modern AC Motor Drives," Energies, vol. 15, no. 19, 2022. https://doi.org/10.3390/en1519....
| eISSN: | 2956-3860 |
| ISSN: | 1507-2711 |
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