Recursive variational mode extraction and its application in rolling bearing fault diagnosis

Abstract The variational mode extraction (VME) developed on the similar basis of variational mode decomposition (VMD) can effectively separate a specific mode by knowing an approximate center frequency from the multi-component signal. Compared with VMD, VME has made certain progress in improving the extraction accuracy and reducing the computational cost when the aim is to separate a specific mode. As the fault feature signal of rolling bearing is usually a band-limited signal which is compact around the excited resonant frequency, VME becomes a potentially effective tool for extracting fault characteristics of rolling bearing. However, how to adaptively determine the center frequency and the penalty factor are two difficult problems when using VME to separate the desired component. Accordingly, this paper presents the recursive variational mode extraction (RVME), an iterative VME-based signal decomposition algorithm. At each iteration of RVME, the initial center frequency and penalty factor for the reconstruction of a specific sub-component can be adaptively determined according to the dominant frequency of the residual signal of the previous iterative decomposition, which makes RVME an adaptive signal decomposition algorithm. The presented method is applied to simulated and experimental fault signals and compared with other classical fault feature extraction approaches, such as variational mode decomposition (VMD) and spectral kurtosis (SK). The results confirm that the established method can extract the fault features as effective as VMD, but it is significantly better than VMD in terms of computational efficiency. Meanwhile, the proposed method shows a stronger capability of weak bearing fault feature extraction and compound fault feature separation compared with SK.

[1]  Jinfeng Zhang,et al.  Periodic impulses extraction based on improved adaptive VMD and sparse code shrinkage denoising and its application in rotating machinery fault diagnosis , 2019, Mechanical Systems and Signal Processing.

[2]  Alessandro Fasana,et al.  The Autogram: An effective approach for selecting the optimal demodulation band in rolling element bearings diagnosis , 2018 .

[3]  Yanxue Wang,et al.  Research on variational mode decomposition and its application in detecting rub-impact fault of the rotor system , 2015 .

[4]  Bin Yang,et al.  An intelligent fault diagnosis approach based on transfer learning from laboratory bearings to locomotive bearings , 2019, Mechanical Systems and Signal Processing.

[5]  Jun Wang,et al.  Nonconvex Group Sparsity Signal Decomposition via Convex Optimization for Bearing Fault Diagnosis , 2020, IEEE Transactions on Instrumentation and Measurement.

[6]  Yonggang Xu,et al.  A Novel Rolling Bearing Fault Diagnosis Method Based on Empirical Wavelet Transform and Spectral Trend , 2020, IEEE Transactions on Instrumentation and Measurement.

[7]  Qiang Miao,et al.  A parameter-adaptive VMD method based on grasshopper optimization algorithm to analyze vibration signals from rotating machinery , 2018, Mechanical Systems and Signal Processing.

[8]  Wenhua Du,et al.  Research and application of improved adaptive MOMEDA fault diagnosis method , 2019, Measurement.

[9]  Zhe Cheng,et al.  Enhanced symplectic characteristics mode decomposition method and its application in fault diagnosis of rolling bearing , 2020 .

[10]  M. Hestenes.  Multiplier and gradient methods , 1969 .

[11]  I. Daubechies,et al.  Synchrosqueezed wavelet transforms: An empirical mode decomposition-like tool , 2011 .

[12]  Changqing Shen,et al.  A new l0-norm embedded MED method for roller element bearing fault diagnosis at early stage of damage , 2018, Measurement.

[13]  Bin Pang,et al.  Rolling Bearing Fault Diagnosis Based on SVDP-Based Kurtogram and Iterative Autocorrelation of Teager Energy Operator , 2019, IEEE Access.

[14]  Zhiliang Liu,et al.  ACCUGRAM: A novel approach based on classification to frequency band selection for rotating machinery fault diagnosis. , 2019, ISA transactions.

[15]  J. Antoni.  The spectral kurtosis: a useful tool for characterising non-stationary signals , 2006 .

[16]  Dominique Zosso,et al.  Variational Mode Decomposition , 2014, IEEE Transactions on Signal Processing.

[17]  Lei Ni,et al.  An improved variational mode decomposition method based on particle swarm optimization for leak detection of liquid pipelines , 2020 .

[18]  Yonghao Miao,et al.  Application of sparsity-oriented VMD for gearbox fault diagnosis based on built-in encoder information. , 2020, ISA transactions.

[19]  He Wang,et al.  An integrated method based on hybrid grey wolf optimizer improved variational mode decomposition and deep neural network for fault diagnosis of rolling bearing , 2020 .

[20]  Zhiwei Wang,et al.  An improved complementary ensemble empirical mode decomposition with adaptive noise and its application to rolling element bearing fault diagnosis. , 2019, ISA transactions.

[21]  Yao Cheng,et al.  Blind deconvolution assisted with periodicity detection techniques and its application to bearing fault feature enhancement , 2020 .

[22]  Yaguo Lei,et al.  Application of an improved maximum correlated kurtosis deconvolution method for fault diagnosis of rolling element bearings , 2017 .

[23]  Zhipeng Feng,et al.  Planet bearing fault diagnosis using multipoint Optimal Minimum Entropy Deconvolution Adjusted , 2019, Journal of Sound and Vibration.

[24]  Haiyang Pan,et al.  Symplectic geometry mode decomposition and its application to rotating machinery compound fault diagnosis , 2019, Mechanical Systems and Signal Processing.

[25]  Jérôme Antoni,et al.  The infogram: Entropic evidence of the signature of repetitive transients , 2016 .

[26]  Jianhui Lin,et al.  A modified scale-space guiding variational mode decomposition for high-speed railway bearing fault diagnosis , 2019, Journal of Sound and Vibration.

[27]  Mojtaba Nazari,et al.  Variational Mode Extraction: A New Efficient Method to Derive Respiratory Signals from ECG , 2018, IEEE Journal of Biomedical and Health Informatics.

[28]  Pietro Borghesani,et al.  A statistical methodology for the design of condition indicators , 2019, Mechanical Systems and Signal Processing.

[29]  R. Tyrrell Rockafellar,et al.  A dual approach to solving nonlinear programming problems by unconstrained optimization , 1973, Math. Program..

[30]  Adam Glowacz,et al.  Early fault diagnosis of bearing and stator faults of the single-phase induction motor using acoustic signals , 2018 .

[31]  Changqing Shen,et al.  Fault diagnosis of rotating machines based on the EMD manifold , 2020 .

[32]  Fulei Chu,et al.  A new SKRgram based demodulation technique for planet bearing fault detection , 2016 .

[33]  Jia Minping,et al.  Application of CSA-VMD and optimal scale morphological slice bispectrum in enhancing outer race fault detection of rolling element bearings , 2019, Mechanical Systems and Signal Processing.

[34]  Jing Lin,et al.  Identification of mechanical compound-fault based on the improved parameter-adaptive variational mode decomposition. , 2019, ISA transactions.

[35]  Zhiwei Wang,et al.  Particle swarm optimization algorithm to solve the deconvolution problem for rolling element bearing fault diagnosis. , 2019, ISA transactions.