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Dictionary Learning Via a Mixed Noise Model for Sparse Representation Classification of Rolling Bearings
Rotating machinery contains a great number of rolling bearings, which play an indispensable role. However, bearing vibration signals in complex environments are often mixed with various noises, which makes it difficult to extract fault characteristics from original signals. It is still challenging to identify the fault types of rolling bearings. To address this issue, a dictionary learning method based on a mixed noise model for the sparse representation classification of rolling bearingsdoi:10.1109/access.2020.3040209 fatcat:ltg37o6rnnerncrk72ej3rgm2u