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International Journal for Research Trends and Innovation
International Peer Reviewed & Refereed Journals, Open Access Journal
ISSN Approved Journal No: 2456-3315 | Impact factor: 8.14 | ESTD Year: 2016
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Issue: March 2026
Volume 11 | Issue 3
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Impact Factor : 8.14
Issue per Year : 12
Volume Published : 11
Issue Published : 118
Article Submitted : 21574
Article Published : 8528
Total Authors : 22430
Total Reviewer : 805
Total Countries : 159
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| Paper Title: | Enhanced Bearing Fault Diagnosis Using SVM and ANN with Feature Selection |
| Authors Name: | NAVEDH AKHTAR JAMALI N , PRAVINRAHUL S K , RANJITHKUMAR S , P Pushparaj |
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IJRTI_206924
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| Published Paper Id: | IJRTI2511084 |
| Published In: | Volume 10 Issue 11, November-2025 |
| DOI: | |
| Abstract: | The early detection of bearing faults in rotating machinery is essential for minimizing downtime and maintenance costs in industrial systems. This project proposes a novel approach that integrates non-contact vibration data acquisition, advanced signal processing, and machine learning techniques to achieve accurate fault prediction. Initially, the collected vibration signals are denoised using the Hilbert Transform, ensuring improved signal clarity. Principal Component Analysis (PCA) is then applied for dimensionality reduction, followed by Sequential Floating Forward Selection (SFFS) for selecting the most discriminative features. These optimized features are classified using Support Vector Machines (SVM) and Artificial Neural Networks (ANN) to detect and categorize various bearing faults. Additionally, the use of Fuzzy Convolution Neural Networks (FCNN) enables the model to effectively handle heterogeneous sensing data and uncertainty in IoT environments. Experimental evaluation demonstrates that ANN achieves superior performance compared to SVM, KNN, and Decision Tree models, delivering high accuracy and robust fault classification. This work highlights the effectiveness of combining fuzzy logic, convolutional neural networks, and IoT data fusion to create a reliable and proactive fault prediction framework for modern industrial applications |
| Keywords: | Machine Learning, Fault Prediction, Fuzzy Convolution Neural Network, IoT, Hilbert Transform, PCA, SFFS, Vibration Analysis |
| Cite Article: | "Enhanced Bearing Fault Diagnosis Using SVM and ANN with Feature Selection", International Journal for Research Trends and Innovation (www.ijrti.org), ISSN:2455-2631, Vol.10, Issue 11, page no.a710-a715, November-2025, Available :http://www.ijrti.org/papers/IJRTI2511084.pdf |
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2456-3315 | IMPACT FACTOR: 8.14 Calculated By Google Scholar| ESTD YEAR: 2016 An International Scholarly Open Access Journal, Peer-Reviewed, Refereed Journal Impact Factor 8.14 Calculate by Google Scholar and Semantic Scholar | AI-Powered Research Tool, Multidisciplinary, Monthly, Multilanguage Journal Indexing in All Major Database & Metadata, Citation Generator |
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Published Paper ID: IJRTI2511084
Registration ID:206924
Published In: Volume 10 Issue 11, November-2025
DOI (Digital Object Identifier):
Page No: a710-a715 Country: ERODE, Tamil Nadu, India Research Area: Engineering Publisher : IJ Publication Published Paper URL : https://www.ijrti.org/viewpaperforall?paper=IJRTI2511084 Published Paper PDF: https://www.ijrti.org/papers/IJRTI2511084 |
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ISSN: 2456-3315
Impact Factor: 8.14 and ISSN APPROVED,
Journal Starting Year (ESTD) : 2016
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