Research on Fault Diagnosis Technology of Electronic Products Based on Artificial Intelligence
Keywords:
Artificial Intelligence, Fault Diagnosis, Machine Learning, Deep LearningAbstract
This paper studies the fault diagnosis technology of electronic products based on artificial intelligence, and constructs an efficient fault diagnosis model by using machine learning and deep learning algorithms. Through steps such as data preprocessing, feature extraction and model training, the actual product fault data is analyzed, and fault mode recognition is realized by using technologies such as support vector machine and convolutional neural network. The simulation results show that the fault diagnosis system based on artificial intelligence technology has a high accuracy rate. For example, in the battery fault diagnosis, the accuracy rate of the system reaches 92.5%, and the fault prediction can be completed in a short time (150 - 180 ms). Compared with traditional methods, the artificial intelligence system significantly improves the diagnosis accuracy and efficiency, and has a lower implementation cost. The research results of this paper show that artificial intelligence has broad application prospects in the field of electronic product fault diagnosis.
References
Li, Wanxin. "User-Centered Design for Diversity: Human-Computer Interaction (HCI) Approaches to Serve Vulnerable Communities." Journal of Computer Technology and Applied Mathematics 1.3 (2024): 85-90.
Shen, Zepeng, et al. "Research on Application of Whale Optimization Algorithm in Financial Payment Fraud Detection." 2025 4th International Conference on Artificial Intelligence, Internet and Digital Economy (ICAID). IEEE, 2025.
J. Choudhury and C. Shi, “Enhanced Performance of Finite Boundary Isolation Forest (FBIF) for Datasets with Standard Distribution Properties,” in 2022 International Conference on Electrical, Computer and Energy Technologies (ICECET), 2022, pp. 1–5, doi: 10.1109/ICECET55527.2022.9873022.
Gao, W. (2026). Construction of an Intelligent Quality and Operations Control System for Industrial Electrical Enterprises Driven by Dual Standards ISO 14001 and ISO 9001. Journal of Industrial Engineering and Applied Science, 4(1), 1-7.
Meng , L. (2023). Research on the Evaluation System of Green Cabling of Cables Based on Neural Network. Innovation & Technology Advances, 1(2), 25–31. https://doi.org/10.61187/ita.v1i2.37
Junxi, Y., Wang, Z., & Chen, C. (2024). GCN-MF: A graph convolutional network based on matrix factorization for recommendation. Innovation & Technology Advances, 2(1), 14–26. https://doi.org/10.61187/ita.v2i1.30
Hu, H., Zhang, J., & Sun, Y. (2024). The Multiscale Deep Neural Networks: Unveiling New Directions in Text Sentiment Analysis. Innovation & Technology Advances, 2(2), 34–45. https://doi.org/10.61187/ita.v2i2.65
Song, C., Jiang, K., Hu, R., Tao, W., Diao, S., & Ma, H. (2025). NTM-CLIP: Natural to Medical CLIP with Dual Semantic Missing Contrastive Learning. Available at SSRN 7071238.
Jiang, K., Yuan, H., Zhang, T., Sun, L., Huang, S., & Xu, I. (2025). UMH-ID: Unifying Multi-Expert Attention and Hard Contrastive Learning for Robust IoT Intrusion Detection. Available at SSRN 7071219.
Zhang, X. (2026, March). A Hybrid LSTM-GARCH Model Integrating Volatility Factors from the US Financial Markets. In Proceedings of the 2026 International Conference on AI Decision-Making and Management (pp. 183-189).
Shen, Z., Lin, S., Wang, Y., Saunders, E., & Dai, Y. (2026, May). Survival Risk Prediction Model for Colorectal Cancer Patients Based on Graph Convolutional Network and TCGA Multi-Omics Data Integration. In 2026 7th International Seminar on Artificial Intelligence, Networking and Information Technology (AINIT) (pp. 370-373). IEEE.
Ma, J. (2025). A Unified Framework for Congestion Diagnosis and Dynamic Mitigation in Complex Networks. International Journal of Advance in Applied Science Research, 4(11), 36-41.
Jin, L. (2025). Optimization of Order Allocation Algorithms for Industrial Internet Platforms. International Journal of Advance in Applied Science Research, 4(12), 44-48.
Miao, J. (2026). Big Data Technologies for Enhanced Network Security Analysis: Applications and Approaches. International Journal of Advance in Applied Science Research, 5(4), 16-20.
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2026 Yin Xiao, Gu Yan
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
