Enhanced seeded region growing algorithm and its application in signal decomposition

Abstract

The components in an engineering signal may overlap in the frequency domain, which causes great inconvenience and even errors in traditional one-dimensional signal processing methods. To identify and extract components in the signal from the time-frequency domain, this paper proposes a time-frequency domain signal decomposition method based on enhanced seeded region growing (ESRG). This paper extends the seeded region growing method to adaptive recognition of target regions in the time-frequency representation and divides the time-frequency representation into several time-frequency components. ESRG can reduce the influence of noise, expand the self-adaptability of time-frequency decomposition and avoid errors caused by manually setting the growth point and threshold. The results of the numerical verification and decomposition of bat echo signals show that this method can accurately decompose multi-component non-stationary signals. The decomposition results of rotor fault and rolling bearing fault signals show that this method can be successfully applied to fault diagnosis.