As essential parts of various types of rotor machinery, rolling element bearings are vital in connecting rotor and support. When rotating, bearing failure is the most common reason of mechanical failure. To maintain the normal running of mechanical components, the detection of bearing failure is extremely important. In this study, a technology for early fault diagnosis of rolling element bearings based on vibration signals was proposed, and the following states of bearing, including normal rolling element bearing, faulty outer ring bearing, faulty inner ring bearing and rolling element failure bearing, were obtained. MSAF-20- MAX (Method of Selection of Amplitudes of Frequency-20-Maximum) was discussed as a feature extraction method, which created feature vectors classified by KNN (K-nearest Neighbour classifier), K- MEANS and SVM (Support Vector Machine).


    Access

    Check access

    Check availability in my library

    Order at Subito €


    Export, share and cite



    Title :

    Early Fault Diagnosis of Bearing Faults Using Vibration Signals


    Contributors:


    Publication date :

    2021-10-20


    Size :

    1593596 byte




    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English



    Separation and Diagnosis of the Early Faults Vibration Signal of the Rolling Bearing

    Xu, H. / Wang, L. / Luo, Y. | British Library Online Contents | 1998


    Vibration-based fault detection of sharp bearing faults in helicopters

    Girondin, Victor / Morel, Herve / Caesar, Jean-Philippe et al. | Tema Archive | 2012



    Ball Bearing Fault Diagnosis Based on Vibration Signals of Two Stroke IC Engine Using Continuous Wavelet Transform

    Ravikumar, K. N. / Madhusudana, C. K. / Kumar, Hemantha et al. | TIBKAT | 2020


    Faults Diagnosis of Bearing Using Mathematical Morphology Method

    Hao, Rujiang / Lu, Wenxiu / Chu, Fulei | AIAA | 2008