Automatic quantitative diagnosis for rolling bearing compound faults via adapted dictionary free orthogonal matching pursuit

Vibration signals of rolling bearings can reflect not only the concrete faulty component, but also the faulty status, e.g., the severity. Motivated by the fault status assessment, a sparse step-impact characteristic-oriented quantitative diagnosis method is developed for the automatic size estimations of compound faults. First, based on the excitation mechanism of bearing step-impact signals, a right-angle quadrilateral model is established for size estimation. Then the linear time-invariant (LTI) filter and autoregressive model (AR model) are used to pre-process the original fault signal. Technically supported by the adapted dictionary free orthogonal matching pursuit (ADOMP), through correlating the Asymmetric Gaussian Chirplet Model (AGCM) atoms and bearing signals, the location of step-impact points are parameterized and the selection strategy for the most suitable AGCM atoms are also investigated, guaranteeing the successful separation of severe and slight fault step-impact components. Finally, the estimations for compound fault sizes are automatically realized via combining the time shift factors of AGCM atoms and proposed right-angle quadrilateral model. The simulated result shows that the proposed right-angle quadrilateral model and quantitative diagnosis method can be reliably applied to the automatic estimations of compound fault sizes. The experimental results show that, the estimation deviations for compound sizes (2.5 mm and 1.0 mm) come only to 1.61% and 6.49% at 300r/min, respectively, superior to Symlet5 wavelet decomposition. Furthermore, the proposed method is applied to the quantitative analysis at different speeds and compound fault sizes, and the satisfactory diagnosis results are obtained. (C) 2020 Elsevier Ltd. All rights reserved.