BEARING FAULT MONITORING SYSTEM USING ACOUSTIC EMISSION

Detecting mechanical faults in bearings and machinery has long been
recognized as being important for preventing catastrophic failure and effective
maintenance planning. The human senses of sound and touch were the first
mechanisms used to detect machinery problems. Electronic sensors have since
offered the ability to feel and listen to machinery with more precision, at more
locations, and over more time than was ever before possible. Interpretation of the
electronic signals delivered by sensors has provided the maintenance engineer with
the diagnostic information necessary to pinpoint bearing faults, thus enabling a more
efficient and predictable maintenance effort. However, skilled and trained personnel
have been required to effectively interpret this diagnostic information. As electronic
sensors have become more sophisticated, and also have the diagnostic techniques,
leading to the ability of earlier detection of failures with less required skill. This
project proposes an implementation of bearing fault monitoring system by using
acoustic emission (AE). The signal is captured and analyzed by MATLAB software.
Generally this project is carried Oft by detecting ultrasonic waves captured by AE
sensor of tested bearing and interprets the data which mean either the bearing is
healthy or havin~ defect. The benefit of having early detection of bearing fault will be
able to save maintenance cost and fso human life.