Novel self-adaptive vibration signal analysis: Concealed component decomposition and its application in bearing fault diagnosis

Abstract

Rolling Elements Bearing (REB) failures are the most common reason for breakdowns in rotating machine parts. The reduction of frequent catastrophes of such rotating components is essential to have maximum productivity. To have a successful prediction of health conditions, performance assessment, and fault diagnosis of rolling element bearing, a valid signal processing method is a basic need. This paper proposes a novel self-adaptive signal decomposition technique: Concealed component decomposition (CCD). The proposed decomposition preserves transitory evidence about perilous signal points and basis waves, with a transient objective equivalent to the timescale of extrema events in the signal. The method isolates and uses the intrinsic instantaneous amplitude in balance with other essential configuration features. The proposed CCD technique is utilized to develop a precise bearing fault diagnosis model. A practical mode selection criterion is also proposed. The selected mode function then undergoes the implementation of envelope spectrum analysis. The proposed model has been validated over different simulated and experimental datasets of different fault types. The proposed method demonstrates its superiority to other existing signal decomposition approaches on various grounds.