Adaptive Wavelet Transform and SVM-based Fault Diagnosis with PSO Optimization in Industrial IoT
Abstract
This paper proposes an innovative joint algorithm based on adaptive wavelet transform (AWT) and support vector machine (SVM) to diagnose data equipment faults in the industrial Internet of Things and optimize the model parameters through particle swarm optimization (PSO) technology. Firstly, adaptive wavelet transform is used to extract time-frequency features of sensor data by adaptively adjusting the wavelet transform according to the wavelet basis function, thereby realizing the extraction of the time-frequency characteristics of the signal. Secondly, an improved support vector machine is used to classify feature data. At last, PSO is applied to improve the precision and efficiency of classification. The experiment results show that the new algorithm has higher precision, computing speed and faster response than the conventional single algorithm. The experimental results show that the proposed AWT-SVM-PSO algorithm achieves an average accuracy improvement of 13% over traditional methods, with the classification accuracy of different fault modes reaching 98%, and the response time is shortened from 300 milliseconds to 200 milliseconds. This project's research results will effectively improve industrial equipment's fault diagnosis capability and provide reliable support for large-scale data processing and real-time monitoring.
Full Text:
PDFReferences
Shang, H., Lu, D., & Zhou, Q. (2021). Early warning of enterprise finance risk of big data mining in Internet of Things based on fuzzy association rules. Neural Computing and Applications, 33(9), 3901-3909.
Jieyang, P., Kimmig, A., Dongkun, W., Niu, Z., Zhi, F., Jiahai, W., et al. (2023). A systematic review of data-driven approaches to fault diagnosis and early warning. Journal of Intelligent Manufacturing, 34(8), 3277-3304.
Chi, Y., Dong, Y., Wang, Z. J., Yu, F. R., & Leung, V. C. (2022). Knowledge-based fault diagnosis in industrial Internet of Things: A survey. IEEE Internet of Things Journal, 9(15), 12886-12900.
Selim, G. E. I., Hemdan, E. E. D., Shehata, A. M., & El-Fishawy, N. A. (2021). Anomaly events classification and detection system in critical industrial Internet of Things infrastructure using machine learning algorithms. Multimedia Tools and Applications, 80(8), 12619-12640.
Yu, W., Liu, Y., Dillon, T., Rahayu, W., & Mostafa, F. (2021). An integrated framework for health state monitoring in a smart factory employing IoT and big data techniques. IEEE Internet of Things Journal, 9(3), 2443-2454.
Maturi, M. H., Gonaygunta, H., Nadella, G. S., & Meduri, K. (2023). Fault diagnosis and prognosis using IoT in Industry 5.0. International Numeric Journal of Machine Learning and Robots, 7(7), 1-21.
Dzaferagic, M., Marchetti, N., & Macaluso, I. (2021). Fault detection and classification in industrial IoT in case of missing sensor data. IEEE Internet of Things Journal, 9(11), 8892-8900.
Zhong, G., Xiong, K., Zhong, Z., & Ai, B. (2021). Internet of Things for high-speed railways. Intelligent and Converged Networks, 2(2), 115-132.
Menon, V. G., Verma, S., Kaur, S., & Sehdev, P. S. (2021). Internet of Things-based optimized routing and big data gathering system for landslide detection. Big Data, 9(4), 289-302.
Zhong, Y., Chen, L., Dan, C., & Rezaeipanah, A. (2022). A systematic survey of data mining and big data analysis in Internet of Things. The Journal of Supercomputing, 78(17), 18405-18453.
Song, C., Liu, S., Han, G., Zeng, P., Yu, H., & Zheng, Q. (2022). Edge-intelligence-based condition monitoring of beam pumping units under heavy noise in industrial Internet of Things for Industry 4.0. IEEE Internet of Things Journal, 10(4), 3037-3046.
Paramesha, M., Rane, N. L., & Rane, J. (2024). Big data analytics, artificial intelligence, machine learning, Internet of Things, and blockchain for enhanced business intelligence. Partners Universal Multidisciplinary Research Journal, 1(2), 110-133.
Wu, Y., Dai, H. N., & Tang, H. (2021). Graph neural networks for anomaly detection in industrial Internet of Things. IEEE Internet of Things Journal, 9(12), 9214-9231.
Long, J., Liang, W., Li, K. C., Wei, Y., & Marino, M. D. (2022). A regularized cross-layer ladder network for intrusion detection in industrial Internet of Things. IEEE Transactions on Industrial Informatics, 19(2), 1747-1755.
Huo, R., Zeng, S., Wang, Z., Shang, J., Chen, W., Huang, T., et al. (2022). A comprehensive survey on blockchain in industrial Internet of Things: Motivations, research progresses, and future challenges. IEEE Communications Surveys & Tutorials, 24(1), 88-122.
Lv, Z., Wu, J., Li, Y., & Song, H. (2022). Cross-layer optimization for industrial Internet of Things in real scene digital twins. IEEE Internet of Things Journal, 9(17), 15618-15629.
Haghnegahdar, L., Joshi, S. S., & Dahotre, N. B. (2022). From IoT-based cloud manufacturing approach to intelligent additive manufacturing: Industrial Internet of Things—an overview. The International Journal of Advanced Manufacturing Technology, 119(3), 1461-1478.
Alam, T. (2022). Blockchain cities: The futuristic cities driven by blockchain, big data, and Internet of Things. GeoJournal, 87(6), 5383-5412.
Khan, A. A., Bourouis, S., Kamruzzaman, M. M., Hadjouni, M., Shaikh, Z. A., Laghari, A. A., et al. (2023). Data security in healthcare industrial Internet of Things with blockchain. IEEE Sensors Journal, 23(20), 25144-25151.
DOI: https://doi.org/10.31449/inf.v49i24.8529
This work is licensed under a Creative Commons Attribution 3.0 License.