Fault Diagnosis of Spindle Device in Hoist Using Variational Mode Decomposition and Statistical Features

By analyzing nonlinear and nonstationary vibration signals from the spindle device of the mine hoist, it is a challenge to overcome the difficulty of fault feature extraction and accurately identify the fault of rotor-bearing system. In response to this problem, this paper proposes a new approach based on variational mode decomposition (VMD), SVM, and statistical characteristics such as variance contribution rate (VCR), energy entropy (EE), and permutation entropy (PE). Comparisons have gone to evaluate the performance of rolling bearing defect by using EMD (Empirical Mode Decomposition), MEEMD (Modified Ensemble EMD), BP (Back Propagation) network, single or multiple statistical characteristics, and different motor loads. The experiment was carried out on the mechanical failure simulator of the main shaft device of the hoist, which verified the reliability and effectiveness of the method. The results show that the diagnosis method is suitable for feature extraction of bearing fault signals, with the highest diagnosis accuracy. It can provide a good practical reference for the fault diagnosis of mechanical equipment of the hoist spindle device and has certain practical value.

[1]  Brigitte Chebel-Morello,et al.  Application of empirical mode decomposition and artificial neural network for automatic bearing fault diagnosis based on vibration signals , 2015 .

[2]  Chuan Li,et al.  Continuous-scale mathematical morphology-based optimal scale band demodulation of impulsive feature for bearing defect diagnosis , 2012 .

[3]  Yangyang Zhang,et al.  Design on Intelligent Diagnosis System of Reciprocating Compressor Based on Multi-agent Technique , 2012 .

[4]  Yaguo Lei,et al.  EEMD method and WNN for fault diagnosis of locomotive roller bearings , 2011, Expert Syst. Appl..

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

[6]  Corinna Cortes,et al.  Support-Vector Networks , 1995, Machine Learning.

[7]  Radoslaw Zimroz,et al.  A new feature for monitoring the condition of gearboxes in non-stationary operating conditions , 2009 .

[8]  Li Li,et al.  Multi-fault diagnosis study on roller bearing based on multi-kernel support vector machine with chaotic particle swarm optimization , 2014 .

[9]  Jiakai Ding,et al.  Gear Fault Diagnosis Based on Genetic Mutation Particle Swarm Optimization VMD and Probabilistic Neural Network Algorithm , 2020, IEEE Access.

[10]  Jong-Duk Son,et al.  Fault diagnosis of low speed bearing based on relevance vector machine and support vector machine , 2009, Expert Syst. Appl..

[11]  Yanbing Wang.  Rock Dynamic Fracture Characteristics Based on NSCB Impact Method , 2018 .

[12]  Shuting Wan,et al.  Teager Energy Entropy Ratio of Wavelet Packet Transform and Its Application in Bearing Fault Diagnosis , 2018, Entropy.

[13]  Yang Yu,et al.  A roller bearing fault diagnosis method based on EMD energy entropy and ANN , 2006 .

[14]  Ming Zhang,et al.  Research on variational mode decomposition in rolling bearings fault diagnosis of the multistage centrifugal pump , 2017 .

[15]  Daming Lin,et al.  A review on machinery diagnostics and prognostics implementing condition-based maintenance , 2006 .

[16]  Wei Li,et al.  Fault diagnosis of rotating machinery with a novel statistical feature extraction and evaluation method , 2015 .

[17]  Hong Fan,et al.  Rotating machine fault diagnosis using empirical mode decomposition , 2008 .

[18]  N. Huang,et al.  The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis , 1998, Proceedings of the Royal Society of London. Series A: Mathematical, Physical and Engineering Sciences.

[19]  Engin Avci,et al.  Speech recognition using a wavelet packet adaptive network based fuzzy inference system , 2006, Expert Syst. Appl..

[20]  Chang Liu,et al.  Study on planetary gear fault diagnosis based on variational mode decomposition and deep neural networks , 2018, Measurement.

[21]  B. Pompe,et al.  Permutation entropy: a natural complexity measure for time series. , 2002, Physical review letters.

[22]  Wei Li,et al.  Robust condition monitoring and fault diagnosis of rolling element bearings using improved EEMD and statistical features , 2014 .

[23]  Jun Shen,et al.  Vibration fault diagnosis of wind turbines based on variational mode decomposition and energy entropy , 2019, Energy.

[24]  P. Tse,et al.  Machine fault diagnosis through an effective exact wavelet analysis , 2004 .

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

[26]  Wei Li,et al.  A novel sensor fault diagnosis method based on Modified Ensemble Empirical Mode Decomposition and Probabilistic Neural Network , 2015 .

[27]  Fan Jiang,et al.  An Improved VMD With Empirical Mode Decomposition and Its Application in Incipient Fault Detection of Rolling Bearing , 2018, IEEE Access.

[28]  Na Zhao,et al.  Gear fault feature extraction and diagnosis method under different load excitation based on EMD, PSO-SVM and fractal box dimension , 2019, Journal of Mechanical Science and Technology.

[29]  Zhiheng Li,et al.  A Deep Transfer Model With Wasserstein Distance Guided Multi-Adversarial Networks for Bearing Fault Diagnosis Under Different Working Conditions , 2019, IEEE Access.

[30]  Robert B. Randall,et al.  Rolling element bearing diagnostics using the Case Western Reserve University data: A benchmark study , 2015 .

[31]  Robert B. Randall,et al.  The spectral kurtosis: application to the vibratory surveillance and diagnostics of rotating machines , 2006 .

[32]  Qinghua Zhang,et al.  Fault Diagnosis of a Rolling Bearing Using Wavelet Packet Denoising and Random Forests , 2017, IEEE Sensors Journal.

[33]  Jing Tian,et al.  Motor Bearing Fault Detection Using Spectral Kurtosis-Based Feature Extraction Coupled With K-Nearest Neighbor Distance Analysis , 2016, IEEE Transactions on Industrial Electronics.

[34]  Chenxi Liu,et al.  Combined Failure Diagnosis of Slewing Bearings Based on MCKD-CEEMD-ApEn , 2018 .