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Gear Fault Diagnosis Using SVM Based on Empirical Mode Decomposition

Publication Type : Conference Paper

Source : 2023 International Conference on Recent Trends in Electronics and Communication (ICRTEC), Mysore, India, 2023, pp. 1-4

Url : https://ieeexplore.ieee.org/document/10111852

Campus : Coimbatore

School : School of Artificial Intelligence

Center : Center for Computational Engineering and Networking

Year : 2023

Abstract : Vibration analysis is one of the major techniques in the condition monitoring of mechanical components. In vibration analysis, the vibration data were taken from earlier conducted experiments for healthy conditions using vibration sensors positioned at various locations. These datasets were imported to MATLAB for feature extraction and analysis. The gear fault-related features in the vibration signals are extracted by Empirical Mode Decomposition (EMD). Further, features like RMS, kurtosis, and skewness were extracted. The extracted features were classified in MATLAB classification algorithms like SVM. The confusion matrix plot will be used to distinguish between the classifiers. The confusion matrix uses both trained data and raw data for comparison and the accuracy of these algorithms was compared.

Cite this Research Publication : Akhil V M, P. P. Mangaji, R. N. Murthy, R. D, S. Hegde and S. V. Devadiga, "Gear Fault Diagnosis Using SVM Based on Empirical Mode Decomposition," 2023 International Conference on Recent Trends in Electronics and Communication (ICRTEC), Mysore, India, 2023, pp. 1-4, doi:10.1109/ICRTEC56977.2023.10111852.

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