Fault Diagnosis of Rolling Element Bearings Based on Adaptive Mode Extraction

Abstract

Generally speaking, vibration signals collected by sensors always contain complex frequency components, which will bring great trouble to bearing condition monitoring and fault diagnosis. A reliable fault signal component extraction method is significant to detect the fault-induced weak repetitive transients. Therefore, many signal decomposition or extraction methods have been developed and are widely employed in fault diagnosis. Based on the recently proposed variational mode extraction (VME) method, an adaptive optimal mode extraction method was designed with a new strategy to extract the mode center frequency and a novel indicator to optimize the balance parameter. The spectrum is first divided into several modes by enveloping curve fitting (ECF), and the center frequencies of each mode are extracted, respectively. All potential fault modes are then extracted sequentially utilizing the extracted center frequency and fixed balance parameter. For the extracted modes, the kurtosis index is applied to select the target mode. Finally, the relative amplitude ratio (RAR) index is used to adaptively adjust the balance parameter. The comparison results reveal that the adaptive mode extraction method can extract the weak fault feature under strong interference.