Rolling bearing early composite fault feature extraction method based on progressive VMD

Abstract

The invention discloses a rolling bearing early composite fault feature extraction method based on progressive VMD. The method comprises the steps of 1, installing an acceleration sensor near a rolling bearing to collect vibration signals during bearing operation; 2, decomposing the acquired vibration signal by using a progressive VMD to obtain a series of decomposed modes; 3, screening the modesobtained after decomposition by using a dual screening criterion based on kurtosis and an energy fluctuation factor to determine a fault mode, and realizing reconstruction of the fault mode; 4, initializing the range and the search step length of the balance parameter alpha, repeating the step 2 and the step 3 for different alpha values, calculating the EFF value of the reconstruction mode, the maximum EFF value corresponding to the optimal alpha value, and then determining the reconstruction mode under the optimal alpha value; and 5, demodulating the reconstructed mode by using TEO to obtaina TEO spectrum, and obtaining a diagnosis result in combination with the bearing related fault characteristic frequency. On the basis of an EEMD recursion idea, the invention provides a progressive decomposition VMD method.

Description

technical field [0001] The invention belongs to the technical field of condition monitoring and fault diagnosis of rotating mechanical equipment, and in particular relates to a progressive VMD-based feature extraction method for early complex faults of rolling bearings. Background technique [0002] As an important component of rotating machinery, bearings are prone to failure due to long-term work under harsh conditions of high speed and alternating loads, resulting in downtime or catastrophic accidents. Therefore, the research on fault diagnosis of rolling bearings has an important reality. significance. In actual engineering, the vibration signals of rolling bearings are non-stationary and doped with strong background noise and multiple interference sources. At the same time, the early composite fault signals are weak, which brings great challenges to the extraction of fault features. Therefore, enhancing fault signal features is very important for improving diagnosis. P...

AI Technical Summary

Problems solved by technology

VMD was widely introduced into rotating machinery fault diagnosis shortly after it was put forward, but the performance of VMD is strictly limited by the number of decomposition modes K and the balance parameter α. ), Genetic Algorithm (GA) has been proposed by domestic scholars, but the parameter optimization process is too time-consuming, it is difficult to be well applied and promoted in actual engineering

At the same time, for the screening of fault modes, the commonly used indicators are kurtosis and cross-correlation coefficient, but the rolling bearing fault signal often contains random impact, which makes the kurtosis index sometimes invalid; in addition, for weak fault signals, the fault mode contained in it The cross-correlation coefficient with the original signal is low, and it is easy to eliminate the fault mode by using the traditional cross-correlation coefficient, which leads to wrong judgment

[0004] Domestic patents involving the application of VMD methods in fault diagnosis include "Weak Fault Diagnosis of Rolling Bearings Based on PSO-VMD-MCKD" (201810610048.2), using the peak factor of the envelope spectrum as the fitness function, using PSO to calculate the key parameters K and α in VMD Optimizing, but the optimization process is too time-consuming, and at the same time, it is difficult to distinguish low-frequency signals and fault shock signals by using the peak factor of the envelope spectrum as a fitness function

The national invention patent "Mechanical Fault Diagnosis Method Based on Parameter Adaptive VMD" (201810259122.0) preliminarily sets the range of K and α, and then determines the optimal key parameters (K and α) through the grid search algorithm. Similarly, The grid search with two parameters will greatly increase the optimization time, which is difficult to apply in practice

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