A non-parametric complementary ensemble empirical mode decomposition (NPCEEMD) is proposed for identifying bearing defects using weak features. NPCEEMD because, unlike existing methods such as decomposition, it does not require defining the ideal SNR of noise and number ensembles, every time while processing signals. The simulation results show that mixing in less than methods. After conducting...

In this study, a novel model that performs ensemble empirical mode decomposition (EEMD) and stepwise regression was developed to forecast the water level of tidal river. Unlike more complex hydrological models, main advantage proposed is only required data are data. EEMD used decompose signals from river into several intrinsic functions (IMFs). These IMFs then reconstruct ocean stream component...