FOREX Press I. J. of Electrical & Electronics Research
Support Open Access
  • Home/
  • IJEER-130217

Research Article |

Optimized Fuzzy SVM with Chaotic Henry Gas Solubility Algorithm for Fault Identification in Rotating Machinery

Author(s): Dr. Mohan S B1, Dr. Prajith Prabhakar2*,Dr. Yokesh V3, M Bharathi4, Dr. Gayathry S Warrier5, Dr Mahalakshmi J6

Published In : International Journal of Electrical and Electronics Research (IJEER)        Volume 13, Issue 2

Publisher : FOREX Publication

Published : 30 June 2025

e-ISSN : 2347-470X

Page(s) : 325-336

DOI: https://doi.org/10.37391/IJEER.130217

Abstract

Reliable and accurate fault diagnosis in rotating machinery is vital for minimizing unplanned downtime, reducing maintenance costs, and ensuring operational safety in industrial environments. Traditional diagnostic approaches depend heavily on manual feature extraction from vibration signals, which can be time-consuming, expertise-dependent, and prone to missing subtle fault patterns. This study presents a novel hybrid framework—IDL-OFSVM—that combines Intelligent Deep Learning (IDL) with an Optimized Fuzzy Support Vector Machine (OFSVM) for automated fault classification. Vibration signals are first transformed using the Continuous Wavelet Transform (CWT), and deep features are extracted via the lightweight MobileNet architecture. The Chaotic Henry Gas Solubility Optimization (CHGSO) algorithm significantly enhances the classification model's performance, which effectively tunes the FSVM parameters. Experimental evaluations on benchmark datasets show that the proposed method achieves 99.8% training and 99.7% testing accuracy, outperforming several state-of-the-art approaches. Beyond technical accuracy, the framework offers practical advantages, including reduced dependency on domain expertise, suitability for real-time monitoring, and potential integration into predictive maintenance systems. These benefits make the IDL-OFSVM model a promising solution for industrial fault diagnosis applications, where reliability, speed, and scalability are crucial.

Keywords: Fault diagnosis, Rotating machinery, Fuzzy Support Vector Machines, Parameter tuning, Vibration signals, Fuzzy logic.




Dr. Mohan S B,Associate Professor, Department Of ECE, S A Engineering College, Chennai, India; Email: drsbmohan@gmail.com

Dr. Prajith Prabhakar,Department of Smart Materials, Saveetha School of Engineering, Saveetha Institute of Medical and Technical Science, Chennai, India; Email: prajithprabhakar15@gmail.com

Dr. Yokesh V ,Department of Electronics and Communication Engineering, Sathyabama Institute of Science and Technology, Chennai, India; Email: yokesh.inba@gmail.com

M Bharathi ,Assistant Professor, Department of Electronics and Communication Engineering, Jeppiaar Institute of Technology, Sriperumbudur, India; Email: bharathimjbn@gmail.com

Dr. Gayathry S Warrier,Assistant Professor, Department of Computer Science, Christ University Bangalore, Karnataka, India; Email: gayathry.warrier@christuniversity.in

Dr Mahalakshmi J ,Assistant Professor, Department of Computer Science, Christ University Bangalore, Karnataka, India; Email: mahalakshmi.j@christuniversity.in

    [1] R. Liu, B. Yang, E. Zio, and X. Chen, “Artificial intelligence for fault diagnosis of rotating machinery: A review,” Mech. Syst. Signal Process., vol. 108, pp. 33–47, 2018.
    [2] H. Shao, H. Jiang, L. Ying, and X. Li, “A novel method for intelligent fault diagnosis of rolling bearings using ensemble deep auto-encoders,” Mech. Syst. Signal Process., vol. 102, pp. 278–297, 2018.
    [3] S. Satpathy, S. Debbarma, A. S. C. Sengupta, and B. K. D. Bhattacharyya, "Design an FPGA, fuzzy based, insolent method for predicting multi-diseases in a rural area," J. Intell. Fuzzy Syst., vol. 37, no. 5, pp. 7039–7046, 2019.
    [4] Q. Wu et al., "Incipient Fault Diagnosis Method via Joint Adaptive Signal Decomposition," in IEEE Sensors Journal, vol. 24, no. 15, pp. 24308-24318, 1 Aug.1, 2024, doi: 10.1109/JSEN.2024.3414299.
    [5] Liu, Qiang, Liu, Songyong, Dai, Qianjin, Yu, Xiao, Teng, Daoxiang, Wei, Ming, Data-Driven Approaches for Diagnosis of Incipient Faults in Cutting Arms of the Roadheader, Shock and Vibration, 2021, 8865068, 15 pages, 2021.
    [6] P. Subbulakshmi, “Mitigating eavesdropping by using fuzzy based MDPOP-Q learning approach and multilevel Stackelberg game theoretic approach in wireless CRN,” Cogn. Syst. Res., vol. 52, pp. 853–861, 2018, doi: 10.1016/j.cogsys.2018.09.021.
    [7] H. Chen, B. Jiang, S. X. Ding, N. Lu and W. Chen, "Probability-Relevant Incipient Fault Detection and Diagnosis Methodology with Applications to Electric Drive Systems," in IEEE Transactions on Control Systems Technology, vol. 27, no. 6, pp. 2766-2773, Nov. 2019, doi: 10.1109/TCST.2018.2866976.
    [8] S. Neelakandan, “An automated exploring and learning model for data prediction using balanced CA-SVM,” J. Ambient Intell. Humaniz. Comput., vol. 12, no. 5, 2020, doi: 10.1007/s12652-020-01937-9.
    [9] Y. Wang, Z. He, and Y. Zi, “Enhancement of signal denoising and multiple fault signatures detecting in rotating machinery using dual-tree complex wavelet transform,” Mech. Syst. Signal Process., vol. 24, pp. 119–137, 2010.
    [10] S. Satpathy, S. Das, and S. Debbarma, “A new healthcare diagnosis system using an IoT-based fuzzy classifier with FPGA,” J. Supercomput., vol. 76, no. 8, pp. 5849–5861, 2020, doi: 10.1007/s11227-019-03013-2.
    [11] Sai, NV Midhun, M. S. Saravanan, and P. Subramanian. "A novel network packet loss detection framework using random forest algorithm over support vector machine learning algorithms to improve accuracy." 2022 International Conference on Knowledge Engineering and Communication Systems (ICKES). IEEE, 2022.
    [12] Raju, K. Sagar, and V. Amudha. "Early Detection and Quantification of Osteoarthritis Severity in Knee Using Support Vector Machine with Improved Accuracy Compared to Convolutional Neural Network." 2023 Eighth International Conference on Science Technology Engineering and Mathematics (ICONSTEM). IEEE, 2023.
    [13] R. R. Bhukya, B. M. Hardas, T. Ch., et al., “An automated word embedding with parameter tuned model for web crawling,” Intell. Autom. Soft Comput., vol. 32, no. 3, pp. 1617–1632, 2022.
    [14] Z. Chen, K. Gryllias, and W. Li, “Mechanical fault diagnosis using convolutional neural networks and extreme learning machines,” Mech. Syst. Signal Process., vol. 133, p. 106272, 2019.
    [15] S. Guo, T. Yang, W. Gao, and C. Zhang, “A novel fault diagnosis method for rotating machinery based on a convolutional neural network,” Sensors, vol. 18, no. 5, p. 1429, 2018.
    [16] W. Gong et al., “A novel deep learning method for intelligent fault diagnosis of rotating machinery based on improved CNN-SVM and multichannel data fusion,” Sensors, vol. 19, no. 7, p. 1693, 2019.
    [17] M. Z. Hasan and F. Al-Turjman, “Optimizing multipath routing with guaranteed fault tolerance in Internet of Things,” IEEE Sens. J., vol. 17, no. 19, pp. 6463–6473, Oct. 2017, doi: 10.1109/JSEN.2017.2739188.
    [18] W. Jiang, C. Wang, J. Zou, and S. Zhang, “Application of deep learning in fault diagnosis of rotating machinery,” Processes, vol. 9, no. 6, p. 919, 2021.
    [19] X. Wu, Y. Zhang, C. Cheng, and Z. Peng, “A hybrid classification autoencoder for semi-supervised fault diagnosis in rotating machinery,” Mech. Syst. Signal Process., vol. 149, p. 107327, 2021.
    [20] R. M. Souza et al., “Deep learning for diagnosis and classification of faults in industrial rotating machinery,” Comput. Ind. Eng., vol. 153, p. 107060, 2021.
    [21] Alamanda, Sophia, et al. "Machine Learning-Based Fault Diagnosis for Rotating Machinery in Industrial Settings." 2024 Ninth International Conference on Science Technology Engineering and Mathematics (ICONSTEM). IEEE, 2024.
    [22] H. Shaodong, D. Ziyang, C. Junsheng, and J. Hongkai, “Intelligent fault diagnosis among different rotating machines using novel stacked transfer auto-encoder optimized by PSO,” ISA Trans., vol. 105, pp. 308–319, 2020.
    [23] A. G. Howard et al., “Mobilenets: Efficient convolutional neural networks for mobile vision applications,” arXiv preprint arXiv:1704.04861, 2017.
    [24] Y. Fan, C. Zhang, Y. Xue, and J. Wang, "A bearing fault diagnosis using a support vector machine optimized by the self-regulating particle swarm," Shock Vib., vol. 2020, Article ID 9096852, 11 pages, 2020, doi: 10.1155/2020/9096852.
    [25] M. R. Kumar, V. C. S. Rao, R. Anand, and H. Singh, “Interpretable filter based convolutional neural network (IF-CNN) for glucose prediction and classification using PD-SS algorithm,” measurement, vol. 183, 2021, doi: 10.1016/j.measurement.2021.109804.
    [26] C. Al-Atroshi, V. K. Nassa, B. Geetha, S. Neelakandan, et al., “Deep learning-based skin lesion diagnosis model using dermoscopic images,” Intell. Autom. Soft Comput., vol. 31, no. 1, pp. 621–634, 2022.
    [27] P. Boyapati, S. Neelakandan, A. A. A. Akeji, A. K. S. Pundir, and R. Walia, “LSGDM with biogeography-based optimization (BBO) model for healthcare applications,” J. Healthc. Eng., vol. 2022, Article ID 2170839, 11 pages, 2022, doi: 10.1155/2022/2170839.
    [28] A. Subasi, “Medical decision support system for diagnosis of neuromuscular disorders using DWT and fuzzy support vector machines,” Comput. Biol. Med., vol. 42, no. 8, pp. 806–815, 2012.
    [29] AlShorman, Omar, et al. "Advancements in condition monitoring and fault diagnosis of rotating machinery: A comprehensive review of image-based intelligent techniques for induction motors." Engineering Applications of Artificial Intelligence 130 (2024): 107724.
    [30] J. Gu and S. Lu, “An effective intrusion detection approach using SVM with naïve Bayes feature embedding,” Comput. Secur., vol. 103, p. 102158, 2021.
    [31] V. Savithiri and S. Gomathi, "Fault Detection Algorithms for Improving Accuracy in Solar PV System," 2024 9th International Conference on Communication and Electronics Systems (ICCES), Coimbatore, India, 2024, pp. 203-207, doi: 10.1109/ICCES63552.2024.10859624.
    [32] S. P. Kala, P. Kumar S, G. Meerimatha, M. Almusawi and S. Gurumoorthy, "Smart Monitoring and Diagnostics for Fault Detection in Power Plant Equipment," 2024 Third International Conference on Distributed Computing and Electrical Circuits and Electronics (ICDCECE), Ballari, India, 2024, pp. 1-4, doi: 10.1109/ICDCECE60827.2024.10548401.

Dr. Mohan S B, Dr. Prajith Prabhakar, Dr. Yokesh V, M Bharathi, Dr. Gayathry S Warrier, and Dr Mahalakshmi J (2025), Optimized Fuzzy SVM with Chaotic Henry Gas Solubility Algorithm for Fault Identification in Rotating Machinery. IJEER 13(2), 325-336. DOI: 10.37391/IJEER.130217.