Complementary ensemble adaptive sparsest narrow-band decomposition method and its applications to the gear crack fault diagnosis

Abstract

Adaptive sparsest narrow-band decomposition is the most sparse solution to search for signals in the over-complete dictionary library containing intrinsic mode functions, which transform the signal decomposition into an optimization problem, but the calculation accuracy must be improved in the case of strong noise interference. Therefore, in combination with the algorithm of the complementary ensemble empirical mode decomposition, a new method of the complementary ensemble adaptive sparsest narrow-band decomposition is obtained. In the complementary ensemble adaptive sparsest narrow-band decomposition, the white noise opposite to the paired symbol is added to the target signal to reduce the reconstruction error and realize the adaptive decomposition of the signal in the process of optimizing the filter parameters. The analysis results of the simulation and experimental data show this method is superior to complementary ensemble empirical mode decomposition and adaptive sparsest narrow-band decomposition in inhibiting the mode confusion, endpoint effect, improving the component orthogonality and accuracy, and effectively identifying the gears fault types.

Intelligent fault diagnosis of gear crack based on side frequency feature under different working conditions

“…Wang [15] used EMD and Teager Kaiser energy operator technology to process the signal and identify the crack fault according to the time-domain and side-frequency characteristics of the processed signal. Peng et al [16] combined with EMD technology, proposed the complementary ensemble adaptive sparsest narrow-band decomposition to diagnose the fault according to the side frequency distribution characteristics of the cracked gear signal. Wang et al [17] found the optimal demodulation subband based on genetic algorithm and then carried out envelope analysis to identify the gear crack fault through the side frequency characteristics of the fault signal.…”