ItemArticle

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

Title
Optimized Fuzzy SVM with Chaotic Henry Gas Solubility Algorithm for Fault Identification in Rotating Machinery
Creator
Mohan, S.B.; Prabhakar, Prajith; Yokesh, V.; Bharathi, M.; Warrier, Gayathry S.; Mahalakshmi, J.
Description
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 frameworkIDL-OFSVMthat 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. 2025 by the Dr. Mohan S B, Dr. Prajith Prabhakar, Dr. Yokesh V, M Bharathi, Dr. Gayathry S Warrier, and Dr Mahalakshmi J.
Source
International Journal of Electrical and Electronics Research;Volume;13;Issue;2;pp.325-336
Date
01-01-2025
Publisher
Forex Publication
Subject
Fault diagnosis; Fuzzy logic; Fuzzy Support Vector Machines; Parameter tuning; Rotating machinery; Vibration signals
Coverage
Mohan S.B., Department Of ECE, S A Engineering College, Chennai, India; Prabhakar P., Department of Smart Materials, Saveetha School of Engineering, Saveetha Institute of Medical and Technical Science, Chennai, India; Yokesh V., Department of Electronics and Communication Engineering, Sathyabama Institute of Science and Technology, Chennai, India; Bharathi M., Department of Electronics and Communication Engineering, Jeppiaar Institute of Technology, Sriperumbudur, India; Warrier G.S., Department of Computer Science, Christ University Bangalore, Karnataka, India; Mahalakshmi J., Department Of ECE, S A Engineering College, Chennai, India
Rights
All Open Access; Hybrid Gold Open Access
Relation
ISSN: 2347470X;
Format
online
Language
English
Type
Article

Citation

Mohan, S.B.; Prabhakar, Prajith; Yokesh, V.; Bharathi, M.; Warrier, Gayathry S.; Mahalakshmi, J., “Optimized Fuzzy SVM with Chaotic Henry Gas Solubility Algorithm for Fault Identification in Rotating Machinery,” CHRIST (Deemed To Be University) Institutional Repository, accessed June 17, 2026, https://archives.christuniversity.in/items/show/23565.

Output Formats