Fault Diagnosis of Rotating Equipment Bearing Based on EEMD and Improved Sparse Representation Algorithm

Abstract

Aiming at the problem that the vibration signals of rolling bearings working in a harsh environment are mixed with many harmonic components and noise signals, while the traditional sparse representation algorithm takes a long time to calculate and has a limited accuracy, a bearing fault feature extraction method based on the ensemble empirical mode decomposition (EEMD) algorithm and improved sparse representation is proposed. Firstly, an improved orthogonal matching pursuit (adapOMP) algorithm is used to separate the harmonic components in the signal to obtain the filtered signal. The processed signal is decomposed by EEMD, and the signal with a kurtosis greater than three is reconstructed. Then, Hankel matrix transformation is carried out to construct the learning dictionary. The K-singular value decomposition (K-SVD) algorithm using the improved termination criterion makes the algorithm have a certain adaptability, and the reconstructed signal is constructed by processing the EEMD results. Through the comparative analysis of the three methods under strong noise, although the K-SVD algorithm can produce good results after being processed by the adapOMP algorithm, the effect of the algorithm is not obvious in the low-frequency range. The method proposed in this paper can effectively extract the impact component from the signal. This will have a positive effect on the extraction of rotating machinery impact features in complex noise environments.

Research on EMD improvement methods

“…At the same time, the white noise is basically canceled after multiple averaging, but there is residual, and the reconstructed noise cannot be ignored. [5] Since EEMD [6] adds Gaussian white noise when improving EMD drawbacks, this method is to get rid of the Gaussian white noise first, and then perform EEMD processing to get the individual IMF components, and then screen the appropriate IMF components for signal reconstruction (a technical means to recover the whole signal with a part of the known signal). The impact of processing Gaussian white noise on signal processing is achieved through three main steps [7] .…”

Electro-mechanical braking system spindle unbalance axis trajectory purification and feedback method

“…In terms of signal filtering, empirical mode decomposition (EMD) is used for axial trajectory purification by decomposing the original signal to obtain IMF components, and then using empirical knowledge to select some relevant IMF components for signal reconstruction, 21 so as to achieve the purpose of axial trajectory purification, so it has good phase preservation and has obvious advantages in axial trajectory applications, but when different IMF components have However, the empirical modal decomposition (EEMD) produces modal conflation when different IMF components have similar time scales; the ensemble empirical modal decomposition (EEMD) uses the underlying logic associated with the modal decomposition, but in order to solve the modal conflation phenomenon, it adds a noise signal to the original signal at the beginning of the calculation for auxiliary analysis, which has achieved good results. 22,23 Therefore, the vibration axis trajectory signals collected during the spindle rotation of the system can be filtered and noise reduced using the ensemble modal decomposition.…”

Remaining Useful Life Prediction of Rolling Bearings Based on PCA and GSACO-SVR Model

“…The construction of the experimental dataset includes two parts: extraction of signal features and feature fusion. 1 has listed the equation and meaning of the root mean square value (RMSV), kurtosis value (KV), peak-to-peak value (PTPV), form factor (FF), margin factor (MF), EEMD [18], the energy value of each intrinsic mode function (IMF), and the Shannon entropy (SE) [19], which are well-known parameters. According to these signal features, we can construct the degradation feature set of rolling bearing signals as…”