Periodic feature oriented adapted dictionary free OMP for rolling element bearing incipient fault diagnosis

摘要

To address the troublesome rolling element bearing incipient fault diagnosis task, it is of great importance to extract the periodic impact components embedded in heavy noise usually. The newly developed sparse representation approach, named adapted dictionary free orthogonal matching pursuit (ADOMP), not only provides a more flexible template - Asymmetric Gaussian Chirplet Model (AGCM), but also breaks through the dependence on essential dictionaries for analytic and learning-based sparse representation approaches. Therefore, ADOMP possesses the potential advantages in analyzing bearing incipient fault vibration signals. Considering the significant influence caused by a manual-selected stopping parameter in standard ADOMP, two pieces of prior knowledge are firstly exploited from the vibration signals. Then through establishing a harmonic product spectrum based kurtosis to directly evaluate the obviousness of extracted periodic features and guide a satisfactory selection for the stopping parameter, the periodic feature oriented ADOMP (PF-ADOMP) is developed. The subsequent refinement by virtue of the exploited time-invariance can further highlight the periodic feature, which is beneficial to the detection of bearing incipient faults. The effectiveness of PF-ADOMP has been validated by analyzing the data from both simulation and bearing life accelerated tests. Meanwhile, the comparisons with respect to the automatic oscillatory behavior-based signal decomposition (Auto-OBSD) method have also been favorably conducted, which show the superiority of the PF-ADOMP in extracting rolling element bearing incipient fault features. (C) 2019 Elsevier Ltd. All rights reserved.