Abstract
It is of great significance for intelligent manufacturing to study condition monitoring and diagnosis methods to realize early diagnosis of mechanical equipment faults. An early fault diagnosis method for rolling bearing based on noise-assisted signal feature enhancement and stochastic resonance was proposed. The advantage of this algorithm is that it avoids the shortage of useful information filtering in traditional noise reduction methods. The generalized multi-scale permutation entropy screening criterion was used to screen the vibration signal, and the parameters of the Duffing vibration subsystem were optimized by the particle swarm optimization algorithm to achieve the optimal matching among the Duffing vibration subsystem, the input signal, and noise, thereby improving the stochastic resonance effect. Part of the background noise energy is transferred to the early weak fault signal feature of the rolling bearing, which enhances the feature of the early weak fault signal. The proposed method was applied to the early fault diagnosis of rolling bearing life state and compared with the adaptive morphology method based on variational mode decomposition (VMD). The results show the effectiveness and feasibility of the proposed method.
Published Version
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