An early diagnosis method for bearing faults based on adaptive frequency-shift variable-scale resonance technology

Abstract

The invention relates to the technical field of bearing fault diagnosis, and discloses an early diagnosis method for bearing faults based on adaptive frequency-shift variable-scale vibration resonance technology. In the method, the original vibration signal of the bearing is firstly extracted, and then the bearing vibration signal is processed by a signal processing function. Generate the superposition signal, and then use the quantum genetic algorithm to adjust the parameters of the bistable potential function for the purpose of improving the signal-to-noise ratio to optimize the superposition signal, so as to obtain a better output waveform. Beneficial effects: the method can use the collected signal to quickly optimize the parameters of the steady-state function through the quantum genetic algorithm, improve the signal-to-noise ratio of the output signal, thereby using the noise to enhance the characteristic signal of the bearing fault, and effectively detect the bearing fault, and the detection effect is better than the traditional one. The maximum correlation spectral kurtosis deconvolution method.

Description

technical field [0001] The invention relates to the technical field of bearing fault diagnosis, in particular to a bearing fault early diagnosis method based on adaptive frequency shift variable scale resonance technology. Background technique [0002] Rolling bearings are commonly used components in mechanical equipment such as hydraulic turbines and wind turbines. During the service process of rolling bearings, they are prone to wear, spalling, pitting and other faults due to external complex load impact. If the faults of rolling bearings are not detected and handled in time, Then the spread of the failure may paralyze the entire equipment, resulting in serious economic losses. Therefore, periodic inspection and maintenance of bearings is required to facilitate risk assessment and component replacement during early failures to reduce possible economic losses. [0003] Existing bearing fault detection mainly relies on fault signal processing technology. Specifically, it is...

AI Technical Summary

Problems solved by technology

[0005] The purpose of the present invention is to provide a bearing fault early diagnosis method based on adaptive frequency shift and variable scale resonance technology, so as to solve the problem that the early fault signal of the bearing is difficult to extract

Images