Performance degradation assessment of rolling bearing under vibration signal monitoring based on optimized variational mode decomposition and improved fuzzy support vector data description

Abstract

Performance degradation assessment methods for rolling bearings under vibration signal monitoring typically involve extracting signal degradation features and inputting them directly into unoptimized assessment models. However, this method often fails to characterize degradation trends and detect early faults in bearings effectively. Moreover, it is susceptible to interference from outliers and false signal fluctuations, posing challenges for accurate performance degradation assessment. To solve the above problems, a novel performance degradation assessment method of rolling bearings based on optimized variational mode decomposition to construct the maximum information degradation feature set and improved hippopotamus optimization algorithm optimized fuzzy support vector data description is proposed. This method effectively suppresses the signal degradation feature pollution caused by the redundancy of irrelevant frequency components, realizes the screening of effective degradation feature sets, and further improves the accuracy of bearing performance degradation assessment. Through experimental verification, this research method uses early healthy rolling bearing samples to establish an assessment model, which can adaptively determine the starting point and degradation trend of bearing degradation. In comparison with other methods for performance degradation assessment under vibration monitoring, it offers distinct advantages.