A novel feature mode decomposition method and its application for gear fault detection

Fault detection can promptly reveal the potential hazards of mechanical equipment, guaranteeing the safety, stability, and reliability of their operation. Although many advanced fault detection methods, such as spectral kurtosis, deconvolution and decomposition, have been developed, most of them still suffer from insufficient utilization of fault features and incomplete extraction of diagnostic information. Given this, we propose a novel method named feature mode decomposition (FMD). Firstly, to coarsely steer the decomposition direction, a finite impulse response (FIR) filter bank is set up with a window initialization. The, correlated kurtosis (CK) is taken to evaluate the latent fault-related information in mode signals, thereby guides the updating process of all adaptive filters, assisted with period estimation. Ultimately, the unnecessary and intermingled modes are weeded out by mode selection. Experimental cases verified that the proposed FMD can adaptively decompose the fault mode of gear fault signal with excellent inspection ability. Comparison between the results and those of the classical variational mode decomposition (VMD) further highlights that FMD is more robust to other interference and noise.