Early detection of bearing faults in rotating machines plays an important role in industrial plants since it avoids the occurrences of serious failure in many parts of the machinery that causes plant failure. It allows the detection of abnormalities and problems at incipient stages which helps in the early of intervention of maintenance and production personnel to keep the plant running and to avoid serious accidents. Therefore, the ability to predict the bearing failure at incipient stage is of great importance. This paper implements the fast Fourier transforms to analyze the acoustic emission time domain signals obtained from acoustic sensor mounted on the bearing housing of an experimental test. Bearing with various health conditions were used in the test. The acquired time domain signals were processed using Lab View and Mat Lab. The results obtained clearly differentiate between the different health conditions of bearings.
Prof. Dr. Bilal BİLGİN