Neural Net for Diagnosis of Antifriction Bearings in Mining Machines

Vibration analysis for diagnosis of machines is a powerful instrument for condition monitoring. Especially for diagnosis of antifriction bearings various equipment and techniques have been developed and are available. The envelope analysis is the most reliable method for bearing diagnosis. Different bearing failures lead to different patterns of the envelope spectrum. Checking the amplitudes of characteristic bearing frequencies is mostly not sufficient for a reliable diagnosis. Therefore human experts compare the pattern of the spectrum with typical patterns of bearing defects. In this paper a diagnosis system is presented which imitates this action of human experts. Using a neural net the system not only distinguishes typical bearing defects but also detects unbalance, beating of disengaged machine parts and other kinds of machine failures.