A Fault Feature Extraction Method for Motor Bearing and Transmission Analysis

Abstract

Abstract:Roller bearings are the most widely used and easily damaged mechanical parts in rotating machinery. Their running state directly affects rotating machinery performance. Empirical mode decomposition (EMD) easily occurs illusive component and mode mixing problem. From the view of feature extraction, a new feature extraction method based on integrating ensemble empirical mode decomposition (EEMD), the correlation coefficient method, and Hilbert transform is proposed to extract fault features and identify fault states for motor bearings in this paper. In the proposed feature extraction method, the EEMD is used to decompose the vibration signal into a series of intrinsic mode functions (IMFs) with different frequency components. Then the correlation coefficient method is used to select the IMF components with the largest correlation coefficient, which are carried out with the Hilbert transform. The obtained corresponding envelope spectra are analyzed to extract the fault feature frequency and identify the fault state by comparing with the theoretical value. Finally, the fault signal transmission performance of vibration signals of the bearing inner ring and outer ring at the drive end and fan end are deeply studied. The experimental results show that the proposed feature extraction method can effectively eliminate the influence of the mode mixing and extract the fault feature frequency, and the energy of the vibration signal in the bearing outer ring at the fan end is lost during the transmission of the vibration signal. It is an effective method to extract the fault feature of the bearing from the noise with interference.

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“…However, the authors concentrated only on rolling bearing faults. Deng et al [31] studied a fault diagnosis method to extract a new feature by combining Hilbert transform coefficients, the correlation coefficients and the ensemble empirical mode decomposition (EEMD). The vibration signal is decomposed into a list of multiple intrinsic mode functions (IMFs) with distinct frequencies using the EEMD.…”

Feature Extraction Algorithm Using a Correlation Coefficient Combined With the VMD and Its Application to the GPS and GRACE

“…Although EEMD and CEEMD can effectively suppress the phenomenon of mode-mixing, many computations bring redundant information, and the VMD decomposition method easily solves the problem of end-effect [30]. In recent years, numerous feature extraction methods have been proposed and are based on signal decomposition algorithms and measuring complexity in different fields [31]- [32]. The VMD and discrete wavelet transform are utilized to classify the signals [33].…”

Denoising and Feature Extraction Algorithms Using NPE Combined with VMD and Their Applications in Ship-Radiated Noise

“…Recently, many new feature extraction algorithms have been developed and are based on signal decomposition algorithms and measuring complexity in different fields [23][24][25][26]. In research [27], a novel fault feature extraction algorithm for rotating equipment is proposed using improved autoregressive minimum entropy deconvolution and VMD, which is proven to be a more powerful algorithm than the existing ones.…”