Abstract
Rolling bearings are widely applied in rotary machines. Bearing failures can lead to long machine downtime and costly maintenance. To reduce the maintenance time and cost, this article proposes a new denoising approach to remove heavy noise and extract weak bearing fault vibration features. The first step proposes a global variational mode decomposition (VMD) optimization algorithm that adaptively matches the optimal decomposition parameters based on kurtosis. In the second step, an adaptive selection criterion for IMF is developed by kurtosis theory. Meanwhile, an adaptive termination criterion is established based on sample entropy (SampEn), and the fault features are extracted with a Butterworth band-pass filter (BBF). Finally, actual fault-bearing experiments are performed using the proposed denoising approach. The comparison with respect to EEMD and WPD is illustrated in detail. The experimental result shows that the proposed approach is a validated tool for the diagnosis of bearings.
Article PDF
Similar content being viewed by others
Avoid common mistakes on your manuscript.
Abbreviations
- VMD :
-
Variational mode decomposition
- AIF :
-
Adaptive iterative filtering
- IMF :
-
Intrinsic mode functions
- SampEn :
-
Sample entropy
- BBF :
-
Butterworth band-pass filter
- EEMD :
-
Ensemble empirical mode decomposition
- WPD :
-
Wavelet packet decomposition
- IVMD :
-
Improved VMD
References
M. Iqbal and A. K. Madan, CNC machine-bearing fault detection based on convolutional neural network using vibration and acoustic signal, Journal of Vibration Engineering and Technologies, 10 (5) (2022) 1613–1621.
B. Liu et al., A review of bearing fault diagnosis for wind turbines, IOP Conference Series: Earth and Environmental Science, 675 (1) (2021) 012094.
H. Shao et al., A novel method for intelligent fault diagnosis of rolling bearings using ensemble deep auto-encoders, Mechanical Systems and Signal Processing, 102 (2018) 278–297.
Z. Wu et al., An adaptive deep transfer learning method for bearing fault diagnosis, Measurement, 151 (2020) 107227.
Z. Liu and L. Zhang, A review of failure modes, condition monitoring and fault diagnosis methods for large-scale wind turbine bearings, Measurement, 149 (2020) 107002.
C. Li et al., A systematic review of deep transfer learning for machinery fault diagnosis, Neurocomputing, 407 (2020) 121–135.
P. Henriquez et al., Review of automatic fault diagnosis systems using audio and vibration signals, IEEE Transactions on Systems Man Cybernetics-Systems, 44 (5) (2014) 642–652.
A. Joshuva et al., An insight on VMD for diagnosing wind turbine blade faults using C4.5 as feature selection and discriminating through multilayer perceptron, Alexandria Engineering Journal, 59 (5) (2020) 3863–3879.
Y. Wang et al., Research on variational mode decomposition and its application in detecting rub-impact fault of the rotor system, Mechanical Systems and Signal Processing, 60 (2015) 243–251.
K. Dragomiretskiy and D. Zosso, Variational mode decomposition, IEEE Transactions on Signal Processing, 62 (3) (2014) 531–544.
X. Zhang et al., A parameter-adaptive VMD method based on grasshopper optimization algorithm to analyze vibration signals from rotating machinery, Mechanical Systems and Signal Processing, 108 (2018) 58–72.
Z. Wang et al., Application of parameter optimized variational mode decomposition method in fault diagnosis of gearbox, IEEE Access, 7 (2019) 44871–44882.
H. Li et al., A bearing fault diagnosis method based on enhanced singular value decomposition, IEEE Transactions on Industrial Informatics, 17 (5) (2021) 3220–3230.
H. Wang et al., A new intelligent bearing fault diagnosis method using SDP representation and SE-CNN, IEEE Transactions on Instrumentation and Measurement, 69 (5) (2020) 2377–2389.
H. Shao et al., Intelligent fault diagnosis of rotor-bearing system under varying working conditions with modified transfer convolutional neural network and thermal images, IEEE Transactions on Industrial Informatics, 17 (5) (2021) 3488–3496.
T. Wang et al., Vibration based condition monitoring and fault diagnosis of wind turbine planetary gearbox: a review, Mechanical Systems and Signal Processing, 126 (2019) 662–685.
A. Cicone, J. Liu and H. Zhou, Adaptive local iterative filtering for signal decomposition and instantaneous frequency analysis, Applied and Computational Harmonic Analysis, 41 (2) (2016) 384–411.
Y. Lei et al., A review on empirical mode decom-position in fault diagnosis of rotating machinery, Mechanical Systems and Signal Processing, 35 (1–2) (2013) 108–126.
X. Ye et al., An adaptive optimized TVF-EMD based on a sparsity-impact measure index for bearing incipient fault diagnosis, IEEE Transactions on Instrumentation and Measurement, 70 (2021) 1–11.
X. Jiang et al., A coarse-to-fine decomposing strategy of VMD for extraction of weak repetitive transients in fault diagnosis of rotating machines, Mechanical Systems and Signal Processing, 116 (2019) 668–692.
L. Fu et al., Condition monitoring for roller bearings of wind turbines based on health evaluation under variable operating states, Energies, 10 (10) (2017) 1564.
Z. Li et al., Independence-oriented VMD to identify fault feature for wheel set bearing fault diagnosis of high-speed locomotive, Mechanical Systems and Signal Processing, 85 (2017) 512–529.
Y. Wang et al., Research on variational mode decomposition and its application in detecting rub-impact fault of the rotor system, Mechanical Systems and Signal Processing, 60 (2015) 243–251.
M. Zhang, Z. Jiang and K. Feng, Research on variational mode decomposition in rolling bearings fault diagnosis of the multistage centrifugal pump, Mechanical Systems and Signal Processing, 93 (2017) 460–493.
J. Li et al., Adaptive energy-constrained variational mode decomposition based on spectrum segmentation and its application in fault detection of rolling bearing, Signal Processing, 183 (2021) 108025.
M. G. A. Nassef, T. M. Hussein and O. Mokhiamar, An adaptive variational mode decomposition based on sailfish optimization algorithm and Gini index for fault identification in rolling bearings, Measurement, 173 (2021) 108514.
Z. Liu and L. Zhang, Naturally damaged wind turbine blade bearing fault detection using novel iterative nonlinear filter and morphological analysis, IEEE Transactions on Industrial Electronics, 67 (10) (2020) 8713–8722.
Acknowledgments
This work was supported by the National Natural Science Foundation of China (No. 52005442), the Zhejiang Provincial Natural Science Foundation of China (No. LY22E050014).
Author information
Authors and Affiliations
Corresponding author
Additional information
Lei Fu was born in Hangzhou, China. He received the Ph.D. degree in Mechanical Engineering from Zhejiang University, Hangzhou, China, in 2018. He is currently a Lecturer with the College of Mechanical Engineering, Zhejiang University of Technology, Hangzhou. His research interests include fault diagnosis, signal processing, and artificial intelligence.
Zepeng Ma was born in Ningbo, China. He received the Bachelor’s degree in Mechanical Engineering from Wenzhou University, Wenzhou, China, in 2021. He is currently working toward the Master’s degree with the College of Mechanical Engineering, Zhejiang University of Technology, Hangzhou. His research interests include fault diagnosis, signal processing, and artificial intelligence.
Yikun Zhang was born in Baoding, China. She is currently working toward the B.E. degree with the College of Mechanical Engineering, Zhejiang University of Technology, Hangzhou. Her research interests include signal processing, and artificial intelligence.
Sinian Wang was born in Suzhou, China. He received the Bachelor’s degree in Mechanical and Automotive Engineering from Bengbu University, Bengbu, China, in 2021. He is currently working toward the Master’s degree with the College of Mechanical Engineering, Zhejiang University of Technology, Hangzhou. His research interests include fault diagnosis, signal processing, and artificial intelligence.
Libin Zhang received the M.S. degree in Agricultural Engineering from Zhejiang Agricultural University, Hanzhou, China. He is currently a Professor with the Zhejiang University of Technology. He has travelled to the University of Bologna, Italy, the University of Rome, the University of Milan, the University of Kiel, and other universities to study and conduct collaborative research. He is primarily engaged in new energy power equipment, robotics and intelligent equipment, and digital printing technology and equipment research.
Rights and permissions
About this article
Cite this article
Fu, L., Ma, Z., Zhang, Y. et al. An improved bearing fault diagnosis method based on variational mode decomposition and adaptive iterative filtering (VMD-AIF). J Mech Sci Technol 37, 1601–1612 (2023). https://doi.org/10.1007/s12206-023-0303-2
Received:
Revised:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s12206-023-0303-2