Enhancing Equipment Health Prediction with Enhanced SMOTE-KNN
DOI:
https://doi.org/10.5281/zenodo.10775533Keywords:
Unbalanced Data, Equipment Life Prediction, SMOTE, Algorithm, KNN AlgorithmAbstract
With the development of industrial automation, the accurate prediction of the health state of equipment becomes particularly important. This study aims to address the problem of application of small sample unbalanced data in device life prediction. A joint optimization model was constructed by combining the modified SMOTE algorithm and the modified KNN algorithm. To solve the sample imbalance problem, the modified KNN algorithm is used to improve the accuracy of classification. Through the simulation analysis of the hydraulic pump status data of Caterpillar Corporation and the vibration data of the water guide bearing of Lingjintan Hydropower Station, the proposed improved algorithm can accurately analyze the running status of the equipment and predict the future healthy development trend. Experimental results show that the joint optimization model has higher accuracy and reliability in the processing of small sample unbalanced data compared with traditional algorithms.
Downloads
Metrics
References
Liu, T., Xu, C., Qiao, Y., Jiang, C., & Chen, W. (2024). News Recommendation with Attention Mechanism. Journal of Industrial Engineering and Applied Science, 2(1), 21-26.
Liu, T., Xu, C., Qiao, Y., Jiang, C., & Yu, J. (2024). Particle Filter SLAM for Vehicle Localization. Journal of Industrial Engineering and Applied Science, 2(1), 27-31.
Xu, C., Qiao, Y., Zhou, Z., Ni, F., & Xiong, J. (2024). Accelerating Semi-Asynchronous Federated Learning. arXiv preprint arXiv:2402.10991.
Zhou, Z. (2024, February). ADVANCES IN ARTIFICIAL INTELLIGENCE-DRIVEN COMPUTER VISION: COMPARISON AND ANALYSIS OF SEVERAL VISUALIZATION TOOLS. In The 8th International scientific and practical conference “Priority areas of research in the scientific activity of teachers”(February 27–March 01, 2024) Zagreb, Croatia. International Science Group. 2024. 298 p. (p. 224).
Xu, C., Yu, J., Chen, W., & Xiong, J. (2024, January). DEEP LEARNING IN PHOTOVOLTAIC POWER GENERATION FORECASTING: CNN-LSTM HYBRID NEURAL NETWORK EXPLORATION AND RESEARCH. In The 3rd International scientific and practical conference “Technologies in education in schools and universities”(January 23-26, 2024) Athens, Greece. International Science Group. 2024. 363 p. (p. 295).
Su, J., Jiang, C., Jin, X., Qiao, Y., Xiao, T., Ma, H., ... & Lin, J. (2024). Large Language Models for Forecasting and Anomaly Detection: A Systematic Literature Review. arXiv preprint arXiv:2402.10350.
Qiao, Y., Jin, J., Ni, F., Yu, J., & Chen, W. (2023). Application of machine learning in financial risk early warning and regional prevention and control: A systematic analysis based on shap. WORLD TRENDS, REALITIES AND ACCOMPANYING PROBLEMS OF DEVELOPMENT, 331.
Qiao, Y., Ni, F., Xia, T., Chen, W., & Xiong, J. (2024, January). AUTOMATIC RECOGNITION OF STATIC PHENOMENA IN RETOUCHED IMAGES: A NOVEL APPROACH. In The 1st International scientific and practical conference “Advanced technologies for the implementation of new ideas”(January 09-12, 2024) Brussels, Belgium. International Science Group. 2024. 349 p. (p. 287).
Ni, F., Zang, H., & Qiao, Y. (2024, January). SMARTFIX: LEVERAGING MACHINE LEARNING FOR PROACTIVE EQUIPMENT MAINTENANCE IN INDUSTRY 4.0. In The 2nd International scientific and practical conference “Innovations in education: prospects and challenges of today”(January 16-19, 2024) Sofia, Bulgaria. International Science Group. 2024. 389 p. (p. 313).
Su, J., Nair, S., & Popokh, L. (2022, November). Optimal Resource Allocation in SDN/NFV-Enabled Networks via Deep Reinforcement Learning. In 2022 IEEE Ninth International Conference on Communications and Networking (ComNet) (pp. 1-7). IEEE.
Zhao, Z., Zhang, N., Xiong, J., Feng, M., Jiang, C., & Wang, X. (2024). Enhancing E-commerce Recommendations: Unveiling Insights from Customer Reviews with BERTFusionDNN. Journal of Theory and Practice of Engineering Science, 4(02), 38-44.
Xiong, J., Feng, M., Wang, X., Jiang, C., Zhang, N., & Zhao, Z. (2024). Decoding sentiments: Enhancing covid-19 tweet analysis through bert-rcnn fusion. Journal of Theory and Practice of Engineering Science, 4(01), 86-93.
Bao, W., Che, H., & Zhang, J. (2020, December). Will_Go at SemEval-2020 Task 3: An accurate model for predicting the (graded) effect of context in word similarity based on BERT. In Proceedings of the Fourteenth Workshop on Semantic Evaluation (pp. 301-306).
Su, J., Nair, S., & Popokh, L. (2023, February). EdgeGym: A Reinforcement Learning Environment for Constraint-Aware NFV Resource Allocation. In 2023 IEEE 2nd International Conference on AI in Cybersecurity (ICAIC) (pp. 1-7). IEEE.
Su, J., Nair, S., & Popokh, L. (2022, November). Optimal Resource Allocation in SDN/NFV-Enabled Networks via Deep Reinforcement Learning. In 2022 IEEE Ninth International Conference on Communications and Networking (ComNet) (pp. 1-7). IEEE.
Huang, X., Zhang, Z., Guo, F., Wang, X., Chi, K., & Wu, K. (2024). Research on Older Adults' Interaction with E-Health Interface Based on Explainable Artificial Intelligence. arXiv preprint arXiv:2402.07915.
Zhang, X., Guo, F., Chen, T., Pan, L., Beliakov, G., & Wu, J. (2023). A Brief Survey of Machine Learning and Deep Learning Techniques for E-Commerce Research. Journal of Theoretical and Applied Electronic Commerce Research, 18(4), 2188-2216.
Li, X., Ye, W., Zou, M., Guo, F., & Huang, Z. (2023, October). Analysis and research of retail customer consumption behavior based on support vector machine. In 2023 IEEE 3rd International Conference on Data Science and Computer Application (ICDSCA) (pp. 713-718). IEEE.
Zhang, Z., Li, P., Hammadi, A. Y. A., Guo, F., Damiani, E., & Yeun, C. Y. (2023). Reputation-Based Federated Learning Defense to Mitigate Threats in EEG Signal Classification. arXiv preprint arXiv:2401.01896.
Peng, Y., Bian, J., & Xu, J. (2024). FedMM: Federated Multi-Modal Learning with Modality Heterogeneity in Computational Pathology. arXiv preprint arXiv:2402.15858.
Wang, L., Bian, J., & Xu, J. (2023). Federated Learning with Instance-Dependent Noisy Labels. arXiv preprint arXiv:2312.10324.
Bian, J., Ren, S., & Xu, J. (2023). CAFE: Carbon-Aware Federated Learning in Geographically Distributed Data Centers. arXiv preprint arXiv:2311.03615.
Bian, J., & Xu, J. (2023, October). Client Clustering for Energy-Efficient Clustered Federated Learning in Wireless Networks. In Adjunct Proceedings of the 2023 ACM International Joint Conference on Pervasive and Ubiquitous Computing & the 2023 ACM International Symposium on Wearable Computing (pp. 718-723).
Bian, J., Shen, C., & Xu, J. (2023). Joint Client Assignment and UAV Route Planning for Indirect-Communication Federated Learning. arXiv preprint arXiv:2304.10744.
Xu, J., & Sen, S. (2023). Ensemble variance reduction methods for stochastic mixed-integer programming and their application to the stochastic facility location problem. INFORMS Journal on Computing.
Xu, J., & Sen, S. (2023). Compromise policy for multi-stage stochastic linear programming: Variance and bias reduction. Computers & Operations Research, 153, 106132.
Liu, S., Wu, K., Jiang, C., Huang, B., & Ma, D. (2023). Financial time-series forecasting: Towards synergizing performance and interpretability within a hybrid machine learning approach. arXiv preprint arXiv:2401.00534.
Wei, K., Zang, H., Pan, Y., Wang, G., & Shen, Z. (2024). Strategic application of ai intelligent algorithm in network threat detection and defense. Journal of Theory and Practice of Engineering Science, 4(01), 49-57.
Zang, H., & Dong, X. (2024). Optimizing Soil Health Management in Smart Agriculture: Deep Learning Algorithms for Nutrient Analysis and Fertilizer Recommendation with Precision Agriculture Systems. Journal of Industrial Engineering and Applied Science, 2(1), 1-7.
Zang, H. (2024). Precision calibration of industrial 3d scanners: An ai-enhanced approach for improved measurement accuracy. Global Academic Frontiers, 2(1), 27-37.
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2024 Journal of Industrial Engineering and Applied Science
This work is licensed under a Creative Commons Attribution 4.0 International License.
