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To diagnose the faults various operating conditions are used in the journal bearing such as Full oil, half loose, half oil, fault 1, fault 2, fault 3 and full loose. The fast Fourier transform is then used to obtain the frequency domain, which gives us the frequency having the highest amplitude. This signal was then decomposed based on the wavelet transform. This was then used as input for a MATLAB code that could plot the time domain signal. The accelerometer is used to collect vibration data, from the journal bearing in the form of time domain. An experimental setup was used to diagnose the faults in the journal bearing. Nowadays, wavelet transformation is one of the most popular technique of the time-frequency-transformations. Therefore, this paper focuses on fault diagnosis on journal bearing using Debauchies Wavelet-02 (DB-02). Thus, it is necessary to provide suitable condition monitoring technique to detect and diagnose failures, and achieve cost savings to the industry.
The major problem that could arise in journal bearings is catastrophic failure due to corrosion or erosion and fatigue, which results in economic loss and creates major safety risks. Journal bearings are widely used to support the shafts in industrial machinery involving heavy loads, such as compressors, turbines and centrifugal pumps.