Self-Adaptive Fault Feature Extraction of Rolling Bearings Based on Enhancing Mode Characteristic of Complete Ensemble Empirical Mode Decomposition with Adaptive Noise

Abstract

Originally, a rolling bearing, as a key part in rotating machinery, is a cyclic symmetric structure. When a fault occurs, it disrupts the symmetry and influences the normal operation of the rolling bearing. To accurately identify faults of rolling bearing, a novel method is proposed, which is based enhancing the mode characteristics of complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN). It includes two parts: the first is the enhancing decomposition of CEEMDAN algorithm, and the second is the identified method of intrinsic information mode (IIM) of vibration signal. For the first part, the new mode functions (CIMFs) are obtained by combing the adjacent intrinsic mode functions (IMFs) and performing the corresponding Fast Fourier Transform (FFT) to strengthen difference feature among IMFs. Then, probability density function (PDF) is used to estimate FFT of each CIMF to obtain overall information of frequency component. Finally, the final intrinsic mode functions (FIMFs) are obtained by proposing identified method of adjacent PDF based on geometrical similarity (modified Hausdorff distance (MHD)). FIMFs indicate the minimum amount of mode information with physical meanings and avoid interference of spurious mode in original CEEMDAN decomposing. Subsequently, comprehensive evaluate index (Kurtosis and de-trended fluctuation analysis (DFA)) is proposed to identify IIM in FIMFs. Experiment results indicate that the proposed method demonstrates superior performance and can accurately extract characteristic frequencies of rolling bearing.

Fault Diagnosis System for Induction Motors by CNN Using Empirical Wavelet Transform

“…The DWT extracts the high-frequency component from the fault current signals and the coefficients of the first scale from the DWT are used to detect the fault. Ma et al [29] proposed a method to extract the features of bearing faults based on the complete ensemble EMD (CEEMD) by enhancing the mode characteristic and via the introduction of adaptive noise to diagnose the bearing faults of rotatory machines. Ge et al [30] proposed a fault diagnosis method based on an empirical wavelet transform sub modal hypothesis test and ambiguity correlation classification to diagnose the rolling bearing faults using vibration signals.…”

An Improved k-Value Symmetrical Difference Analytic Energy Operator With HTFIF and L-KCA for Bearing Fault Diagnosis

“…Deering et al [20] used a masking signal to improve the mode mixing and intermittent issue in the transient process, but its mode separation ability is affected by the signal magnitude [4], [21], [22]. Wu et al [23] and Yeh et al [24] proposed the ensemble EMD (EEMD) and complementary EEMD (CEEMD) methods to improve the mode mixing and sensitivity of the standard EMD, but bring new issues of residue noise and spurious modes [25]. Lin et al [14] used the convolution of the low-pass filter function and the signal itself as the ''signal moving average'' instead of the average between two envelopes in EMD and proposed the iterative filter (IF) algorithm.…”

Fault Feature Extraction and Diagnosis of Rolling Bearings Based on Enhanced Complementary Empirical Mode Decomposition with Adaptive Noise and Statistical Time-Domain Features

“…The proposed method was used to extract fault features under different rotation speeds. Meanwhile, the CELMDAN kurtosis, final intrinsic mode functions-de-trended fluctuation analysis (FIMF-DFA) [38], VMD kurtosis and AR-MED methods were used to extract the fault feature. The data length was 2048 for each test condition.…”