Research on Rolling-Element Bearing Composite Fault Diagnosis Methods Based on RLMD and SSA-CYCBD

Abstract

Aiming at the problem that it is difficult to separate and extract the composite fault features of rolling-element bearings, a composite fault diagnosis method combining robust local mean decomposition (RLMD), sparrow search algorithm (SSA), maximum second-order cyclostationarity blind deconvolution (CYCBD), is proposed. First, the RLMD is used to decompose the product function of the signal, and the two indicators, the excess and the correlation coefficient are then used as evaluation criteria to select the appropriate components for reconstruction. The reconstructed signal is then inputted into the SSA-optimized CYCBD algorithm, by specifying the objective function parameter which separates the faults and obtains multiple single fault signals with optimal noise reduction. Finally, envelope demodulation analysis is used for the multiple single fault signals, to obtain the characteristic frequencies of the corresponding faults, so as to complete the fault separation and feature extraction of composite faults. In order to verify the effectiveness of the method, the initial signals and the actual signals generated by the computer shall be used. The algorithm is verified using the XJTU-SY rolling-element bearing dataset, which shows the good performance of the method.