Journal
MEASUREMENT
Volume 173, Issue -, Pages -Publisher
ELSEVIER SCI LTD
DOI: 10.1016/j.measurement.2020.108580
Keywords
Fault diagnosis; Variational mode decomposition; Time-shift multiscale dispersion entropy; Vibrational Harris hawks optimization; Support vector machine
Funding
- National Natural Science Foundation of China [51741907]
- Open Fund of Hubei Provincial Key Laboratory for Operation and Control of Cascaded Hydropower Station [2017KJX06]
- Research Fund for Excellent Dissertation of China Three Gorges University [2019SSPY070]
Ask authors/readers for more resources
A novel fault diagnosis approach for rolling bearings is proposed by integrating VMD, TSMDE, and SVM optimized by VHHO. Vibration signals are decomposed into IMFs by VMD, TSMDE is employed to extract multiscale fault features, and VHHO is used to optimize SVM parameters for fault recognition, achieving better diagnosis performance.
To fully mine the effective fault information and improve the fault diagnosis accuracy, a novel fault diagnosis approach for rolling bearings is proposed by integrating variational mode decomposition (VMD), time-shift multiscale dispersion entropy (TSMDE) and support vector machine (SVM) optimized by vibrational Harris hawks optimization algorithm (VHHO). Firstly, vibration signals with different fault types are decomposed into several intrinsic mode functions (IMFs) by VMD. Subsequently, the proposed TSMDE aggregating time-shift procedure and dispersion entropy is employed to extract multiscale fault features from IMFs. Afterwards, the proposed VHHO that adopts a periodic mutation mechanism to enhance the original Harris hawks optimization (HHO) is devoted to search the optimal parameters of SVM, with which different faults are recognized. Finally, simulations and applications are conducted to evaluate the proposed coordinated VMD-TSMDE-VHHO-SVM approach, and the results reveal that the proposed approach can achieve better diagnosis performance than other comparative ones.
Authors
I am an author on this paper
Click your name to claim this paper and add it to your profile.
Reviews
Recommended
No Data Available