Noise Eliminated Ensemble Empirical Mode Decomposition Scalogram Analysis for Rotating Machinery Fault Diagnosis

Abstract

Rotating machinery is one of the major components of industries that suffer from various faults due to the constant workload. Therefore, a fast and reliable fault diagnosis method is essential for machine condition monitoring. In this study, noise eliminated ensemble empirical mode decomposition (NEEEMD) was used for fault feature extraction. A convolution neural network (CNN) classifier was applied for classification because of its feature learning ability. A generalized CNN architecture was proposed to reduce the model training time. A sample size of 64×64×3 pixels RGB scalograms are used as the classifier input. However, CNN requires a large number of training data to achieve high accuracy and robustness. Deep convolution generative adversarial network (DCGAN) was applied for data augmentation during the training phase. To evaluate the effectiveness of the proposed feature extraction method, scalograms from related feature extraction methods such as ensemble empirical mode decomposition (EEMD), complementary EEMD (CEEMD), and continuous wavelet transform (CWT) are classified. The effectiveness of scalograms is also validated by comparing the classifier performance using grayscale samples from the raw vibration signals. All the outputs from bearing and blade fault classifiers showed that scalogram samples from the proposed NEEEMD method obtained the highest accuracy, sensitivity, and robustness using CNN. DCGAN was applied with the proposed NEEEMD scalograms to further increase the CNN classifier’s performance and identify the optimal number of training data. After training the classifier using augmented samples, the results showed that the classifier obtained even higher validation and test accuracy with greater robustness. The proposed method can be used as a more generalized and robust method for rotating machinery fault diagnosis.

Machine Fault Detection Using a Hybrid CNN-LSTM Attention-Based Model

“…Regarding noise reduction, Faysal et al [33] proposed a noise-eliminated ensemble empirical mode decomposition (NEEEMD) method for fault diagnosis in rotating machinery. They proved that the NEEEMD could be more generalized and robust for the problem.…”

A Novel Fault Diagnosis Method Based on NEEEMD-RUSLP Feature Selection and BTLSTSVM

“…Where () wrM t is the residual of white noise. More detailed information can be found in reference [47]. The parameter selection method for white noise can be referenced in references [48] and [49], the number of ensembles in this research is 100 and the standard deviation of the white noise is 1 and the mean of it is 0.…”

A Hybrid Model of Ensemble Empirical Mode Decomposition and Sparrow Search Algorithm-Based Long Short-Term Memory Neural Networks for Monthly Runoff Forecasting

“…However, with the significant drawback of mode mixing of EMD, data with similar scales may appear in different intrinsic mode functions (IMFs) (Zhang and Hong, 2019). To overcome this defect, ensemble empirical mode decomposition (EEMD) is proposed by adapting the Gaussian white noise and widely used in many fields, such as hydrological forecasting and mechanical fault diagnosis (Zhang et al, 2018;Ali et al, 2020;Faysal et al, 2021;Wang et al, 2021). For example, Ali et al (2020) investigated the EEMD combined with RF and kernel ridge regression model for monthly rainfall forecasts and verified that the hybrid model could attain better rainfall forecast accuracy.…”