Fault Diagnosis of Rolling Bearings Based on WPE by Wavelet Decomposition and ELM

Abstract

The fault diagnosis classification method based on wavelet decomposition and weighted permutation entropy (WPE) by the extreme learning machine (ELM) is proposed to address the complexity and non-smoothness of rolling bearing vibration signals. The wavelet decomposition based on ‘db3’ is used to decompose the signal into four layers and extract the approximate and detailed components, respectively. Then, the WPE values of the approximate (CA) and detailed (CD) components of each layer are calculated and composed to be the feature vectors, which are finally fed into the extreme learning machine with optimal parameters for classification. The comparative study of the simulations based on WPE and permutation entropy (PE) shows that the classification method of seven kinds of signals of normal bearing signals and six types of fault states (7 mils and 14 mils) based on WPE (CA, CD) with the number of nodes in the hidden layers of ELM determined by the five-fold cross-validation has the best performances, the training accuracy can reach 100%, and the testing accuracy can reach 98.57% with 37 nodes of the hidden layer by ELM. The proposed method using WPE (CA, CD) by ELM provides guidance for the multi-classification of normal bearing signals.

Research on Feature Extraction and Fault Diagnosis Method for Rolling Bearing Vibration Signals Based on Improved FDM-SVD and CYCBD

“…The data used in this study were sourced from the publicly available dataset of the Case Western Reserve University (CWRU) laboratory [36]. The experimental setup is illustrated in Figure 25 [37]. The experiment involved driving a motor, which was equipped with a torque sensor and an encoder on the motor drive shaft.…”

Fault Diagnosis Method of Rolling Bearing Based on ESGMD-CC and AFSA-ELM

“…ELM is a class of single hidden layer feedforward neural networks with fast learning speed and high generalization ability, which is suitable for application in fault diagnosis. Xi et al [22] proposed a feature extraction model based on wavelet decomposition and weighted PE to effectively extract feature vectors and input them into an ELM model with optimal parameters for classification, and achieved better test accuracy. The stability and accuracy of fault diagnosis will be impacted by the ELM's random setting of weights and bias.…”

Bearing Fault Vibration Signal Denoising Based on Adaptive Denoising Autoencoder

“…Over the years, considerable attention has been devoted to reducing noise interference and extracting meaningful fault features [9][10][11]. Various signal denoising techniques have been explored, including empirical mode decomposition (EMD) [12,13], variational mode decomposition (VMD) [14,15], wavelet analysis (WT) [16][17][18], and threshold denoising [19,20]. Despite their usefulness, these methods often lack adaptability, particularly in noisy environments.…”