Effect of unitary sample shifted Laplacian and rectangular distributions in bearing fault identifications of induction motor

Abstract

Fault identifications of bearings are complex process and require non-stationary signal processing methods along with the envelope and cepstral analysis techniques to demodulate the amplitude modulated signals. In this study, the fault signals from the drive end induction motor bearings are detected using proposed unitary sample shifted rectangular and Laplacian probability density functions (pdf). These functions are used to generate recursive frame-based crest factor (FCF), frame-based kurtosis (FKS) and frame-based energy (FE) time series. These time series substantially detect the impulses that depict the fault and shaft rotating frequencies effectively. Apart from detection of fault, the proposed method significantly suppresses the higher order modulated resonating frequencies without the use of non-coherent amplitude demodulation technique. Similarly, the spectral analyses are found to be observable in fault and shaft rotating frequencies identification using rectangular pdf. However, it is observed that the Laplacian pdf is more sensitive in detecting faults and the spectral leakages are lower than rectangular pdf for variable defect sizes. It is also found that the FCF and FKS time series are more probable in detecting fault, whereas FE is significant in depicting shaft rotation.