A Fusion Feature Extraction Method Using EEMD and Correlation Coefficient Analysis for Bearing Fault Diagnosis

Abstract

Acceleration sensors are frequently applied to collect vibration signals for bearing fault diagnosis. To fully use these vibration signals of multi-sensors, this paper proposes a new approach to fuse multi-sensor information for bearing fault diagnosis by using ensemble empirical mode decomposition (EEMD), correlation coefficient analysis, and support vector machine (SVM). First, EEMD is applied to decompose the vibration signal into a set of intrinsic mode functions (IMFs), and a correlation coefficient ratio factor (CCRF) is defined to select sensitive IMFs to reconstruct new vibration signals for further feature fusion analysis. Second, an original feature space is constructed from the reconstructed signal. Afterwards, weights are assigned by correlation coefficients among the vibration signals of the considered multi-sensors, and the so-called fused features are extracted by the obtained weights and original feature space. Finally, a trained SVM is employed as the classifier for bearing fault diagnosis. The diagnosis results of the original vibration signals, the first IMF, the proposed reconstruction signal, and the proposed method are 73.33%, 74.17%, 95.83% and 100%, respectively. Therefore, the experiments show that the proposed method has the highest diagnostic accuracy, and it can be regarded as a new way to improve diagnosis results for bearings.

A Hybrid Energy Feature Extraction Approach for Ship-Radiated Noise Based on CEEMDAN Combined with Energy Difference and Energy Entropy

“…EMD has been widely used in different fields, such as short-term wind speed forecasting combined with hybrid linear and nonlinear models [8], the detection and location of pipeline leakage [9], the detection of incipient damages for truss structures [10], denoising for grain flow signal [11], biomedical photoacoustic imaging optimization [12] and heart rate variability analysis [13]. Many scholars have also applied EEMD to their research fields, such as wind speed forecasting combined with the cuckoo search algorithm [14], machine feature extraction combined with a kernel-independent component [15], feature extraction for motor bearing combined with multi-scale fuzzy entropy [16], a bearing fault diagnosis combined with correlation coefficient analysis [17], a partial discharge feature extraction combined with sample entropy [18] and monthly streamflow forecasting combined with multi-scale predictors selection [19]. In addition, CEEMDAN is used in machinery, electricity and medicine, such as impact signal denoising [20], daily peak load forecasting [21], health degradation monitoring for rolling bearings combined with multi-scale sample entropy [22], planetary gear fault diagnosis combined with permutation entropy [23], denoising for gear transmission system [24], friction signal denoising combined with mutual information [25] and electrocardiogram signal denoising combined with wavelet threshold [26].…”

Detecting and Learning Unknown Fault States by Automatically Finding the Optimal Number of Clusters for Online Bearing Fault Diagnosis

“…Motor current signature analysis (MCSA) is adopted for fault diagnosis [6,7] because of the fact that it is sensitive to different failures of industrial motor and provides nonintrusive monitoring. Vibration signal analysis-based fault diagnosis is widely used for this purpose, which may provide the most information about bearing failures [8][9][10]. The vibration analysis and MCSA show good performance for the early detection of bearing defects.…”

“…Sci. 2019, 9,2326 3 of 25 background trained model for identifying the new fault. Also, the fault classification model is not updated by incorporating the new fault information.…”

Noise Reduction Method of Underwater Acoustic Signals Based on Uniform Phase Empirical Mode Decomposition, Amplitude-Aware Permutation Entropy, and Pearson Correlation Coefficient

“…Recently, an ocean of noise reduction methods based on signal decomposition algorithms and Shannon entropy have been developed and used in different fields, such as acoustic signal [ 23 , 24 ], hydropower unit vibration signal [ 25 ], bearing vibration signal [ 26 ], medical signal [ 27 ], wind speed prediction [ 28 ], and so on. Xiao et al [ 29 ] proposed a fault denoising and feature extraction method of rolling bearing based on NMD and continuous wavelet transform (CWT).…”