IJBTT

Machinery Faults Diagnosis using Support Vector Machine (SVM) and Naïve Bayes classifiers

Machinery Faults Diagnosis using Support Vector Machine (SVM) and Naïve Bayes classifiers
	© 2022 by IJETT Journal
	Volume-70 Issue-12
	Year of Publication : 2022
	Author : Maamar Ali Saud AL Tobi, Ramachandran. KP., Saleh AL-Araimi, Rene Pacturan, Amuthakannan Rajakannu, Chetha Achuthan
	DOI : 10.14445/22315381/IJETT-V70I12P204

How to Cite?

Maamar Ali Saud AL Tobi, Ramachandran. KP., Saleh AL-Araimi, Rene Pacturan, Amuthakannan Rajakannu, Chetha Achuthan, " Machinery Faults Diagnosis using Support Vector Machine (SVM) and Naïve Bayes classifiers," International Journal of Engineering Trends and Technology, vol. 70, no. 12, pp. 26-34, 2022. Crossref, https://doi.org/10.14445/22315381/IJETT-V70I12P204

Abstract
This work aims to investigate some machinery conditions viz. (healthy, imbalance, misalignment, gear fault, inner bearing fault, outer bearing fault, and ball bearing fault). The machinery conditions are simulated based on real-time vibration data acquired from a Machinery Fault Simulator (MFS). There are three main stages for the diagnosis process, which are the data acquisition, pre-processing (feature extraction), and the classification of the condition based on Artificial Intelligence (AI) classifiers, where the Continuous Wavelet Transform (CWT) method is applied to pre-process the obtained datasets (signals), and extract the features based on five statistical parameters namely: RMS, Kurtosis, Peak, Impulse Factor and Shape Factor. Two classifiers based on Artificial Intelligence (AI) are applied and compared to classify the machinery conditions; namely, Support Vector Machine (SVM) using three different kernels, namely; Radial Basis Function (RBC), Linear and Polynomial, and Naïve Bayes classifiers, and the best number of feature inputs and the best value of some kernel parameters are investigated and identified. The classification accuracy and rate in SVM are evaluated through different evaluation metrics, and the results are compared with the classification rates from Naïve Bayes, where SVM has shown better performance in terms of classification. This work emphasized the novelty by integrating feature extraction methods like CWT with the two different AI classifiers and using different performance evaluation parameters.

Keywords
Machinery Fault Simulator (MFS), Continuous Wavelet Transform (CWT), Support Vector Machine (SVM), Naïve Bayes and Feature Extraction.

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