Papers by Keyword: Bearing Diagnostics

Abstract: Bearing defect is statistically the most frequent cause of an induction motor fault. The research described in the paper utilized the phenomenon of the current change in the induction motor with bearing defect. Methods based on the analysis of the supplying current are particularly useful when it is impossible to install diagnostic devices directly on the motor. The presented method of rolling-element bearing diagnostics used indirect transformation, namely Clark transformation. It determines the vector of the spatial stator current based on instantaneous current measurements of the induction motor supply phases current. The analysis of the processed measurement data used multilayered, one-directional neural networks, which are particularly attractive due to their nonlinear structure and ability to learn. During the research 40 bearings: undamaged, with damages of three types and various degrees of fault extent, were used. The conducted research proves the efficiency of neural networks for detection and recognition of faults in induction motor bearings. In case of tests of the unknown state bearings, an efficiency approach to failure detection equaled 77%.

Abstract: Many machine faults, such as local defects in bearings and gears, manifest themselves in vibration signals as a series of impulsive events. Kurtosis is a measure of the impulsiveness of a signal, and spectral kurtosis (SK) gives an indication of how the kurtosis (of a bandpass filtered signal) varies with frequency. This not only gives an indication of the frequency bands to be processed, but can also be used to generate a filter to extract the most impulsive part of a signal. The first step in calculating SK is to perform a time/frequency decomposition of the signal, and then calculate the kurtosis for each frequency line. The paper compares the original STFT (short time Fourier transform) with wavelet analysis for the time/frequency decomposition, and for determining the optimum combination of centre frequency and bandwidth for maximizing the SK. The paper also describes how the SK can be enhanced by “prewhitening” the signal using an autoregressive (AR) model, this sometimes revealing an incipient fault at a much earlier stage.