Mechanical vibration signal feature extraction method based on combination of stochastic resonance and kernel principal component analysis

Abstract

The invention relates to a mechanical vibration signal feature extraction method based on combination of stochastic resonance and kernel principal component analysis. The method comprises the steps that firstly, the stochastic resonance method is applied for conducting pretreatment on rotor oscillation original signals measured by a sensor, the signal periodicity is improved, and the oscillation signal to noise ratio is improved; then, a time domain feature set is extracted for the pretreated output signals; then the kernel principal component analysis method is adopted for conducting nonlinear feature transformation for the extracted time domain feature set, and therefore the final needed feature set is obtained. The method is applied to feature extraction and failure diagnosis of simulated failure of an engine rotor, the result shows that the feature set extracted through the method is of linear independence, the number of dimensions is smaller, the separability is higher, the precision and efficiency of the failure diagnosis can be effectively improved, and application in engineering practice is facilitated.

Description

technical field [0001] The invention belongs to the field of fault diagnosis of mechanical equipment, and in particular relates to a mechanical vibration signal feature extraction method based on the combination of stochastic resonance and nuclear principal component analysis. Background technique [0002] When structural failures occur in mechanical equipment such as aero-engine rotors, it is an effective means to use vibration signals for diagnosis. However, affected by factors such as the environment and working conditions, the rotor often bears relatively complex loads, and the vibrations generated by the rotating parts also have relatively serious coupling phenomena. In addition, due to the limitations of the sensor's own working conditions, the acquired vibration signals often have certain gaps. noise. Under certain conditions, such as early faults, excessive noise often affects feature extraction and fault discrimination. Therefore, it is a hotspot in the field of r...

AI Technical Summary

Problems solved by technology

However, the obtained time-domain feature set has a relatively simple form, cannot effectively reflect weak and nonlinear fault information, and often has strong linear correlation and other deficiencies.

Images