An Adaptive Spectral Kurtosis Method and its Application to Fault Detection of Rolling Element Bearings

Hu, Yue; Bao, Wenjie; Tu, Xiaotong; Li, Ké

doi:10.1109/tim.2019.2905022

Cited by 66 publications

(33 citation statements)

References 29 publications

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“…Udmale and Singh [56] proposed a novel intelligent diagnosis method using spectral kurtosis and extreme learning machine for fault classification of rotating machines. Hu et al [57] proposed a new and adaptive spectral kurtosis method for the bearing fault detection. Li et al [58] proposed an enhanced FBE (EFBE) adopting WPT as the filter of FBE to overcome the shortcomings of the original FBE.…”

Section: Introductionmentioning

confidence: 99%

Rolling Element Fault Diagnosis Based on VMD and Sensitivity MCKD

Cui

Guan

Chen³

2021

IEEE Access

126

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In order to improve the diagnosis accuracy and solve the weak fault signal of rolling element of rolling bearings due to long transmission path, a novel fault diagnosis method based on variational mode decomposition (VMD) and maximum correlation kurtosis deconvolution (MCKD), namely VMD-MCKD-FD is proposed for rolling elements of rolling bearings in this paper. In the proposed VMD-MCKD-FD, the vibration signal of rolling element of rolling bearings is decomposed into a series of Intrinsic Mode Functions (IMFs) by using VMD method. Then the number of modes with outstanding fault information is determined by Kurtosis criterion in order to calculate the deconvolution period T. The periodic fault component of reconstructed signal is enhanced by using sensitivity MCKD method. Finally, the power spectrum of the reconstructed signal is analyzed in detail in order to obtain the fault frequency and diagnose the rolling element fault of rolling bearings. The simulation signal and actual vibration signal are selected to verify the effectiveness of the VMD-MCKD-FD method. The experimental results show that the VMD-MCKD-FD method can effectively diagnose the rolling element fault of rolling bearings and obtain better fault accuracy.

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Section: Introductionmentioning

confidence: 99%

Rolling Element Fault Diagnosis Based on VMD and Sensitivity MCKD

Cui

Guan

Chen³

2021

IEEE Access

126

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“…Amounts of studies focusing on the construction of optimal band-pass filter (OBF) have been arising in recent years and spectral kurtosis (SK) [1] is the landmark of these methods. An adaptive and flexible SK method being from parameter selection is proposed, and its effectiveness in fault feature extraction of REB is verified thorough simulation and experiment [2]. To solve the problem of kurtosis being vulnerable to impulsive noise, a sparsity index called Gini index is introduced as one substitutes for OBF construction [3].…”

Section: Introductionmentioning

confidence: 99%

Weak fault feature extraction of rolling element bearing based on variational mode extraction and multi-objective information fusion band-pass filter

Wang¹,

Du²,

Li³

et al. 2021

J. vibroeng.

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Aiming at solving the difficulty in extracting early weak fault features of rolling element bearing (REB), a feature extraction method by combing variational mode extraction (VME) with multi-objective information fusion band-pass filter (MIFBF) is proposed. This method is based on the advantage of the VME in filtering out the interference signals and the enhancement effect of the MIFBF on the impact characteristic signals. Firstly, VME is used as the signal preprocessing method to filter out the interference noise. Then, the filtered signal is taken as the target signal, and then MIFBF is used to analyze it to obtain the optimal band-pass filter parameters-center Frequency and bandwidth, thereby constructing the optimal band-pass filter. Finally, further filter the target signal based on the constructed optimal band-pass filter and apply envelope demodulation spectral (EDS) analysis on the filtered signal. At last, satisfactory fault feature extraction effect can be achieved. To obtain key parameters of the optimal band-pass filter, this paper proposes a method based on time and frequency domain fusion indexes to balance the extraction of impact characteristics and cyclostationary characteristics while REB failure occurs. Compared with the existed single index based on time domain or frequency domain, better results could be obtained. Effectiveness and superiority of the proposed method are verified through simulation and experiments.

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“…To improve this problem, variational mode decomposition (VMD) [12] was proposed and has become a research hotspot in signal processing [13]- [15]. Spectral kurtosis (SK) [16], [17] as a filter-based method can locate resonance frequency band accurately and efficiently. Similarly, deconvolution-based method such as maximum correlation kurtosis deconvolution (MCKD) [18] is also filter-based, which can filter out the noise components in the signal.…”

Section: Introductionmentioning

confidence: 99%

Collaborative Double Difference Sparse Regularization and Convex Optimization for Bearing Fault Detection

Liu

Huang

et al. 2021

IEEE Access

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The localized faults of bearings often produce a series of periodic impacts. However, how to extract these repetitive transients from the signals with strong noise interference is still a challenging problem. In this paper, a new method called collaborative double difference sparse regularization (CDDSR) is proposed for bearing fault detection. To be specific, the first and second-order difference matrices are integrated into sparse regularization term, and the differential sparsity and denoising effect of fault signal are enhanced by collaboration. According to the time domain impulse characteristics of fault signal, the sparsity of signal itself will also be considered. Based on the majorization-minimization (MM) algorithm, the objective optimization model can be solved quickly. Furthermore, the selection of regularization parameters is deeply studied, and an adaptive parameter selection strategy is given. The performance of CDDSR is verified through simulation analysis and two experimental cases, and the ability of CDDSR to extract fault features is further evaluated by quantitative index. Results demonstrate its superiority in eliminating noise interference and extracting periodic impulses in comparison to other state-of-the-art methods.

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An Adaptive Spectral Kurtosis Method and its Application to Fault Detection of Rolling Element Bearings

Cited by 66 publications

References 29 publications

Rolling Element Fault Diagnosis Based on VMD and Sensitivity MCKD

Rolling Element Fault Diagnosis Based on VMD and Sensitivity MCKD

Weak fault feature extraction of rolling element bearing based on variational mode extraction and multi-objective information fusion band-pass filter

Collaborative Double Difference Sparse Regularization and Convex Optimization for Bearing Fault Detection

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