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Improved Bearing Fault Diagnosis by Feature Extraction Based on GLCM, Fusion of Selection Methods, and Multiclass-Naïve Bayes Classification

The 
presence of bearing faults reduces the efficiency of rotating machines and thus 
increases energy consumption or even the total stoppage of the machine. It becomes essential to correctly diagnose the 
fault caused by the bearing. Hence the importance of determining an 
effective features extraction method that best describes the fault. The vision 
of this paper is to merge the features selection methods in order to define the 
most relevant featuresin the texture of the 
vibration signal images. In this study, the Gray Level Co-occurrence Matrix 
(GLCM) in texture analysis is applied on the vibration signal represented in 
images. Features selection based on the merge of PCA (Principal component Analysis) method 
and SFE (Sequential Features Extraction) method is done to obtain the most relevant features. The multiclass-Na?ve 
Bayesclassifier is used to test the proposed approach. The success rate 
of this classification is 98.27%. The relevant features obtained give promising 
results and are more efficient than the methods observed in the literature.