Development of Acoustic Emission (AE) based defect parameters for slow rotating roller bearings

Detection of bearing failures is a crucial time-based process amongst all industries. Usually the standard vibration analysis (Fourier analysis, spectrum analysis) is used. In certain cases, the usability of vibration analysis comes to an end. In particular the damage-detection sensitivity of slow rotating bearings (e.g. in the mining industry) is weak and therefore vibration analysis fails to detect failures as soon as possible. The target of this paper is to present high frequency AE based defect parameters in order to detect roller bearing damages at the outer and inner race in a very early stage. Each type of damage is simulated in a test bench and analysed individually, so that the origin of the damage is comprehensible and further damage predictions are reproducible. In a test bench both vibration and AE is measured and compared. Therefore the AE waveform will be presented to show changes in the waveform while a defect is emerging. In order to show the potential of an acoustic emission based analysis, the results of the test bench (vibration and AE) are compared related to changes in the waveform and origin of the defect as well as time to failure detection. The developed parameters are based on new technique that reduce the amount of data required, so that online monitoring of slow rotating roller bearings becomes more manageable. Due to the characteristic of the developed parameters, it is possible to use them in the maintenance of slow rotating machines, so that a failure prediction can be done even before the standard vibration analysis starts to detect a commencing damage.