To overcome the difficulty of early fault diagnosis for the rolling bearing

a method for the fault diagnosis of rolling bearings based on the resonance-based sparse signal decomposition and the tunable-Q wavelet reconstruction was proposed. In this method

the vibration signal of a rolling bearing was decomposed into the high-resonance component and the low-resonance component by the resonance-based sparse signal decomposition. Then

the low-resonance component was further decomposed into a set of sub-signals by the tunable-Q wavelet method and the proper signal was reconstructed from some selected sub-signals combined with kurtosis analysis. Finally

the proper signal was analyzed by the Hilbert demodulation method and the fault characteristics of the rolling bearing could be extracted. Simulation and application examples show that the proposed method is effective in extracting impulse signal from vibration signal of rolling bearing and making the fault characteristics more prominent.