Analysis and Diagnosis of Rolling Bearing Faults from the Perspective of Frequency Domain

Abstract

Abstract Rolling bearing components are broadly utilized in major mechanical fabricating businesses and guaranteeing the secure and steady operation of rolling heading could be a basic necessity of the fabricating prepare. Engineering for intelligent manufacturing has grown significantly in importance during the past several years in the manufacturing sector. The method for identifying mechanical faults based on “frequency domain analysis plus intelligent model” has developed rapidly. In this study, methods such as envelope spectrum analysis and spectral kurtosis are applied to process and analyze fault data to improve the service life of rolling bearing production equipment. In addition, we perform grid search tuning of the hyperparameters in spectral kurtosis, enabling faster frequency band selection for envelope spectral bandpass filtering.