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Bearing fault diagnosis is important in condition monitoring of any rotating machine. Early fault detection in machinery can save millions of dollars in emergency maintenance cost. Different techniques are used for fault analysis such as short time Fourier transforms (STFT), Wavelet analysis (WA), cepstrum analysis, Model based analysis, etc. we have doing detecting bearing faults using FFT and by using Wavelet analysis more specifically wavelet Analysis up to two levels of approximations and detail components. The analysis is carried out offline in MATLAB. Diagnosing the faults before in hand can save the millions of dollars of industry and can save the time as well. It has been found that Condition monitoring of rolling element bearings has enabled cost saving of over 50% as compared with the old traditional methods. The most common method of monitoring the condition of rolling element bearing is by using vibration signal analysis. Measure the vibrations of machine recorded by velocity

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To detect and analyze severity of ball bearing breakdown and prediction before ball bearing breakdown using historical data

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