Rolling bearing fault diagnosis method based on CEEMDAN and GWO-NLM

Abstract

The invention discloses a rolling bearing fault diagnosis method based on CEEMDAN and GWO-NLM, and the method comprises the steps: carrying out the decomposition of a fault signal collected by a sensor through employing CEEMDAN, screening the decomposed IMF components through employing a correlation coefficient-energy ratio-kurtosis criterion, and reconstructing the screened signal into a new signal; performing parameter optimization selection by using a non-local mean filter optimized by a grey wolf algorithm to realize an optimal denoising effect, performing SG smooth filtering on the denoised signal to perform secondary denoising, and performing feature extraction on the finally obtained signal to obtain fault features of the bearing; and finally judging the fault type of the rolling bearing according to the fault characteristics of the bearing. According to the method, noise interference in the vibration signal can be effectively suppressed, and the fault diagnosis accuracy of the bearing is improved.

Description

technical field [0001] The invention relates to the technical field of bearing fault diagnosis, in particular to a rolling bearing fault diagnosis method based on CEEMDAN and GWO-NLM. Background technique [0002] Rolling bearings are widely used in various rotating mechanical systems, and their motion state will have a huge impact on the accuracy, reliability and service life of the overall mechanical system. Since the bearing works at high speed and under high load, it is more prone to failure, and its working state may affect the performance and safety of the entire machine operation. Therefore, fault diagnosis of rolling bearings is essential for health monitoring of rotating machinery systems. This will help reduce costs related to emergency maintenance and production. During the working process of the bearing, due to improper installation, overload and poor lubrication, it is easy to cause damage to the local defects of the bearing. When the bearing fails in the earl...

AI Technical Summary

Problems solved by technology

However, due to the introduction of Gaussian white noise, the final reconstructed signal is affected by residual white noise, and the original signal cannot be reconstructed accurately. Not only that, but in the iterative operation of the algorithm, the calculation amount is large and the decomposition time is long

At present, wavelet transform and blind source separation have been proved to have a good effect on separating noise and fault signals, but there are problems such as local distortion and partial effective information loss.

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