[Durkhure et al., 3(7): July, 2014] ISSN: 2277-9655 Scientific Journal Impact Factor: 3.449 (ISRA), Impact Factor: 1.852 http: // www.ijesrt.com(C)International Journal of Engineering Sciences & Research Technology [711-715] IJESRT INTERNATIONAL JOURNAL OF ENGINEERING SCIENCES & RESEARCH TECHNOLOGY Fault Diagnosis of Ball Bearing using Time Domain Analysis and Fast Fourier Transformation Pravesh Durkhure *1 , Akhilesh Lodwal *2 *1 ME Scholar,Department of Mechanical Engineering, DAVV-IET, Indore, India *2 Assistant Professor, Department of Mechanical Engineering, DAVV-IET, Indore, India praveshdurkhure@gmail.com Abstract In this study Fault diagnosis of Ball bearings is done by statistical analysis under various time domain parameters. The objective of this study is to investigate the correlation between time domain and frequency domain analysis of vibration signature to judge and find the fault in bearing. This is achieved by vibration analysis and investigating different time domain parameter like Kurtosis, Skewness, Crest factor, RMS Value. For this purpose the bearing is coupled to the motor and observation were taken at 810 rpm. Vibration of the bearing are converted in voltage signal (milivolt) using an accelerometer/piezoelectric transducer. The bearing is taken under two different conditions viz Healthy (normal bearing) and Faulty (defected outer race bearing) with the aim of fault detection. Vibration data of healthy bearing are used as a standard for the analysis of vibration spectra of faulty bearing. Vibration signals are analyzed through different operations performed in MATLAB software. The result shows that the statistical analysis through different time domain parameters and its fast Fourier transformation provides efficient representation of fault detection in rolling element bearings. So as an initial stage if we find kurtosis and skewness values it can predict a fault. And if we get higher values of time domain parameters then only it needs to go for its frequency domain analysis. In this paper we also get exact fault position for defective bearing by its frequency domain analysis. Keywords- Rolling Element Bearing, Bearing Fault, Vibration signatures, Fault Diagnosis. Introduction Every rotating machine has the possibility of failure after long period of working. This failure is mainly occurs due to wear in its different parts which converts in some form of vibration at which causes the failure. Every machine requires some maintenance throughout its life to prevent shutdown. Condition monitoring is a technique in which we monitor the condition of machine every time and give it required maintenance. Ball bearing is one of the essential elements of the rotating machinery and one of the important issues in Ball bearing application is the reduction of noise and vibration originating from these bearings. Proper functioning of bearings is most important in nuclear power stations, chemical plants, aviation industries and also process industries. A large survey on faults in the electric motor was carried out by Electric Power Research Institute (EPRI) in 1985 and found that 41% of faults related to worn motor bearings [1]. These bearings generate vibrations during operation even if they are geometrically and elastically perfect. Hence the bearing is the machine component in rotary system those are particularly prone to failures due to uninterrupted operation, heavy load, harsh working conditions etc. So by monitoring the health of a bearing we can prevent any big failure. Therefore detection of these defects and online monitoring of their health condition is important for condition monitoring and fault diagnosis. The aim of this study is to investigate the correlation between vibration analysis and fault diagnosis in a rolling element bearing.