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Fault Diagnosis of Gearbox using Machine Learning and Deep Learning Techniques
Jithin Jose1, O.S. Deepa2, M. Saimurugan3, P. Krishnakumar4, T. Praveenkumar5

1Jithin Jose*, Department of Mechanical Engineering, Amrita School of Engineering, Coimbatore, Amrita Vishwa Vidyapeetham, India.
2O.S. Deepa, Department of Mathamatics, Amrita School of Engineering, Coimbatore, Amrita Vishwa Vidyapeetham, India.
3M. Saimurugan, Department of Mechanical Engineering, Amrita School of Engineering, Coimbatore, Amrita Vishwa Vidyapeetham, India.
4P. Krishnakumar, Department of Mechanical Engineering, Amrita School of Engineering, Coimbatore, Amrita Vishwa Vidyapeetham, India.
5T. Praveenkumar, Department of Automobile Engineering, SRM Institute of Science and Technology, Kattankulathur, Kanchipuram, India.
Manuscript received on September 23, 2019. | Revised Manuscript received on October 15, 2019. | Manuscript published on October 30, 2019. | PP: 3940-3943 | Volume-9 Issue-1, October 2019 | Retrieval Number: A1486109119/2019©BEIESP | DOI: 10.35940/ijeat.A1486.109119
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)

Abstract: Gearbox is an important component used for automobiles, machine tools, industries etc. Failure of any component in gearbox will cause huge maintenance cost and production loss. Failure should be detected as early as possible in order to avoid sudden breakdown which even cause catastrophic failures. Vibration signals are used for machine condition monitoring for predictive maintenance and efficiently predicts fault in the gearbox. In this paper signals from vibration is used for diagnosis of gearbox fault. The experiment uses four different conditions of gearbox in four different load conditions. Then statistical feature extraction is done and obtained result is given to Decision Tree, Support Vector Machine (SVM), Convolutional Neural Network (CNN) and Deep Neural Network (DNN) for fault diagnosis. The efficiency of these four techniques is compared and shows that machine learning is better than deep learning in gearbox fault diagnosis.
Keywords: CNN, DNN, Machine Condition Monitoring.