Publication Type : Journal Article
Publisher : Procedia Engineering
Source : Procedia Engineering, Elsevier, Volume 97, p.2092–2098 (2014)
Url : http://www.sciencedirect.com/science/article/pii/S187770581403522X
Campus : Coimbatore
School : School of Engineering
Center : Computational Engineering and Networking
Department : Mechanical Engineering
Verified : Yes
Year : 2014
Abstract : Gearbox is an essential device employed in industries to vary speed and load conditions according to the requirements. More advancement in its design and operation leads to increase in industrial applications. The failure in any of the components of gearbox can lead to production loss and increase maintenance cost. The component failure has to be detected earlier to avoid unexpected breakdown. Vibration measurements are used to monitor the condition of the machine for predictive maintenance and to predict the gearbox faults successfully. This paper addresses the use of vibration signal for automated fault diagnosis of gearbox. In the experimental studies, good gears and face wear gears are used to collect vibration signals for good and faulty conditions of the gearbox. Each gear is tested with two different speeds and loading conditions. The statistical features are extracted from the acquired vibration signals. The extracted features are given as an input to the support vector machine (SVM) for fault identification. The Performance of the fault identification system using vibration signals are discussed and compared.
Cite this Research Publication : T. Praveenkumar, Dr. Saimurugan M., Krishna Kumar P., and Dr. K. I. Ramachandran, “Fault Diagnosis of Automobile Gearbox Based on Machine Learning Techniques”, Procedia Engineering, vol. 97, pp. 2092–2098, 2014.