Rolling Bearing Fault Feature Extraction Method based on VMD and Fast-Kurtogram

Random impact interference has always been an important subject in fault diagnosis. In fast kurtogram (FK), kurtosis is a sparsity index used to locate optimal resonance frequency bands. However, the shortcomings of kurtosis measure tend to select the random impact with a large amplitude and has a relatively weak sparse measure ability, which reduces the accuracy and anti-interference of the FK method. To overcome the effects of random impact interference, this paper proposes a novel bearing fault diagnosis method named the fast nonlinear Hoyergram (FNH), in which nonlinear Hoyer index is employed to replace kurtosis to improve the fault representation capability under random impule interference. Firstly, Z score normalization and generalized nonlinear sigmoid activation function are used for signal preprocessing, and-the scale distribution of the signal will be changed to weaken the impact of random impact interference. Secondly, the Hoyer index, which can be viewed as the normalized form of the 2⁄ 1norm, is be used to replace kurtosis to improve the sparsity measure capability. Thirdly, in the Hoyergram, the frequency band with the largest Hoyer value can be chosen as the best resonance frequency band for the squared envelope analysis. Finally, the proposed FNH is compared with the FK method through simulation and experimental signals, and the effectiveness of the proposed method is verified.

Section: Introductionmentioning

confidence: 99%

Fast nonlinear Hoyergram for bearings fault diagnosis under random impact interference

Yang

Zhang

Wang

et al. 2022

“…Qin et al combined optimized Morlet wavelet and kurtosis to extract fault features of gears under the influence of strong background noise [11]. Die et al [12] introduced variational mode decomposition (VMD) into the FK method and successfully extracted the fault features of rolling bearings. Guo et al [13] combined the FK method with vibration separation to diagnose the failure of the planetary gear and sun gear of the planetary gearbox.…”

Section: Introductionmentioning

confidence: 99%

Nonlinear fast kurtogram for the extraction of gear fault features with shock interference

Ma¹,

Wang²,

Han³

et al. 2022

The fault feature extraction of gearbox under shock interference is an exceedingly difficult and valuable subject. Effective usage of resonance frequency band is one of the solutions for this subject. However, the existing fast kurtogram (FK) method is prone to misdiagnosis due to the sensitivity of this method to aperiodic shocks. To overcome the sensitivity of the FK method to irrelevant shock, this paper proposes a nonlinear fast kurtogram (NFK) method. First, Z-score normalization is performed on the signal. Then, Sigmoid is used to improve the fault representation under shock interference. Thirdly, the signal is divided into different frequency band, and the band with the largest kurtosis is selected for filtering. Finally, the gear fault is analyzed by the square envelope spectrum. Simulation and experimental verification are used to demonstrated the effectiveness of the proposed method. The results show that the gear fault features with shock interference can be extracted by the NFK method.

“…However, these bearings are subject to a variety of faults during long-term service, particularly under harsh running environments, that can result in machinery breakdown and fatal accidents (Sun et al approaches to extract the local characteristics of vibration signals for bearing fault detection. The predominant analysis approaches employed include empirical mode decomposition (EMD) (Huang et al 1998, Yang et al 2007, wavelet transform (Sun et al 2013), ensemble EMD (EEMD) (Wang et al 2014), and variational mode decomposition (VMD) (Dragomiretskiy and Zosso 2014, Yang et al 2016, Die et al 2019. Here, EMD conducts vibration signal analysis by adaptively decomposing non-stationary signals into several intrinsic mode functions (IMFs).…”

Section: Introductionmentioning

confidence: 99%

Application of an information fusion scheme for rolling element bearing fault diagnosis

Liu

et al. 2021

This study addresses the high level of misdiagnoses and low reliability of individual rolling element bearing fault diagnosis methods by proposing a fault diagnosis scheme with enhanced diagnosis accuracy that combines the results of two individual diagnosis methods based on an improved information fusion method. The proposed scheme applies variational mode decomposition in conjunction with a support vector machine for conducting fault diagnosis in the frequency domain, which achieves high fault diagnosis precision for learned fault conditions. Meanwhile, good generalization ability is achieved for identifying the operational conditions of bearings in the time domain by integrated mathematical morphology and correlation analysis. Subsequently, conflicts arising between evidences derived from the individual detection results are measured comprehensively using a novel strategy, and the evidences are then combined based on the Dempster–Shafer theory (DST) to enhance the information fusion effect. The effectiveness of the proposed information fusion method is verified by means of a numerical example in comparison with other fusion methods based on DST. The experimental application of the proposed information fusion fault diagnosis scheme demonstrates the complementary advantages of the two individual methods for significantly improving the diagnosis accuracy relative to the accuracies of the individual methods alone.