Sparse learning based classification framework for planetary bearing health diagnostics

Planetary bearings are the most complex and challenging components for health diagnostics among wind turbine and helicopter planetary transmissions. Classical spectrum-based diagnosis strategy for planetary bearings faces great challenges because of complex structures and strong modulation effects. This work develops a novel sparse learning based classification (SLBC) framework with the overlapping segmentation strategy to address planetary bearing health diagnostics. Our SLBC framework learns the reconstructive dictionaries for sparse representations of vibration signals and achieves robust recognitions with the sparse approximation criterion. Firstly, the overlapping segmentation strategy is introduced to fully leverage periodic similarity features and augment training datasets for sparse dictionary learning. Then, the category-specific sub-dictionaries are learned from the augmented training datasets in a data-driven fashion via K-singular value decomposition. Finally, intelligent planetary-bearing fault recognition is achieved via the sparse approximation criterion. Our SLBC framework has been verified effective to classify three and four planetary-bearing health states with diagnostic accuracy of 100% and 99.98%, respectively. Comparisons with several advanced approaches have confirmed the superiority of SLBC in diagnostic accuracy, noise robustness, and computation time for planetary bearing health diagnostics.