Broadband Mode Decomposition and Its Application to the Quality Evaluation of Welding Inverter Power Source Signals

This article proposes a novel data-driven adaptive decomposition method named broadband mode decomposition (BMD) for analyzing complex signals containing broadband components, such as square signals and sawtooth signals. For effective broadband signals with “sharp corners,” an unavoidable error occurs when applying former methods, such as variational mode decomposition (VMD) and ensemble empirical mode decomposition (EEMD), due to the Gibbs phenomenon and the interpolation of extreme points. Therefore, we propose BMD for separating the broadband modes, narrowband modes, and noise in a complex nonstationary signal. First, an associative dictionary library consisting of typical broadband signals and narrowband signals is established. Second, a smoothness function is employed as the optimization objective function, and the amplitude, frequency, and phase of the broadband signals and the filter parameters of the narrowband signals are applied as optimization parameters. Third, the sparsest decomposition results are obtained by searching the dictionary using an artificial chemical reaction optimization algorithm. The simulation and experimental signal analyses indicate that BMD is more accurate than EEMD and VMD in extracting broadband components from a noisy signal and that BMD is suitable for quality evaluations of welding inverter power source signals.