Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Faculty Publications Article Faculty Publications -Articles Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Creator An entity primarily responsible for making the resource Mohan, S.B.; Prabhakar, Prajith; Yokesh, V.; Bharathi, M.; Warrier, Gayathry S.; Mahalakshmi, J. Title A name given to the resource Optimized Fuzzy SVM with Chaotic Henry Gas Solubility Algorithm for Fault Identification in Rotating Machinery Date A point or period of time associated with an event in the lifecycle of the resource 01-01-2025 Source A related resource from which the described resource is derived International Journal of Electrical and Electronics Research;Volume;13;Issue;2;pp.325-336 Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.37391/IJEER.130217" target="_blank" rel="noreferrer noopener">https://doi.org/10.37391/IJEER.130217</a> <br /><br /><a href="https://www.scopus.com/pages/publications/105010095596?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105010095596?origin=resultslist</a> Coverage The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant 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 Description An account of the resource 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. Subject The topic of the resource Fault diagnosis; Fuzzy logic; Fuzzy Support Vector Machines; Parameter tuning; Rotating machinery; Vibration signals Publisher An entity responsible for making the resource available Forex Publication Relation A related resource ISSN: 2347470X; Language A language of the resource English Type The nature or genre of the resource Article Rights Information about rights held in and over the resource All Open Access; Hybrid Gold Open Access Format The file format, physical medium, or dimensions of the resource online