Bearing Fault Diagnosis using Support Vector Machine with Genetic Algorithms Based Optimization and K Fold Cross-Validation Method
Rahul Semil1, Pratesh Jaiswal2 

1Rahul Semil, Department of Mechanical Engineering, Madhav Institute of Technology &Science, Gwalior (M.P.), India.
2Pratesh Jaiswal, Department of Mechanical Engineering, Madhav Institute of Technology &Science, Gwalior, (M.P.), India.

Manuscript received on 01 March 2019 | Revised Manuscript received on 05 March 2019 | Manuscript published on 30 July 2019 | PP: 3242-3250 | Volume-8 Issue-2, July 2019 | Retrieval Number: B2828078219/19©BEIESP | DOI: 10.35940/ijrte.B2828.078219
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Abstract: Moving component bearing is utilized to convey radial load and axial load or both just as. REB has nonlinear conduct make issue misalignment, surface waviness, fault happen at the inward race, external race, enclosure, ball or roller, so REB has a restricted life. Our concentration to evacuate fault diagnosis of bearing at the outer race has been investigating. For this purpose, REB vibration analysis is used. This paper present a support vector machine algorithm (SVM) approach with GA (Genetic algorithm) based optimization compare the result with SVM with cross-validation (CV) method along these lines, the information is processed correctly and an exact way. Time-domain Analysis, high pass and low pass filtering etc. used for feature extraction from vibration signal. Further, these feature extraction used as input to the SVM classifier. Support vector machine, a training given projected preparing information, the procedure yield perfect hyperplane. Feature extraction help to provides the actual condition of bearing. In this work, different signal processing techniques and process are used for fault diagnosis of bearing.
Keyword: Rolling Element Bearing, Vibration Analysis, SVM Algorithm, Feature Selection, Condition Monitoring

Scope of the Article: Machine Learning