An integrated method based on hybrid grey wolf optimizer improved variational mode decomposition and deep neural network for fault diagnosis of rolling bearing

Abstract The selection of penalty parameters along with the number of components in the Variational Modal Decomposition (VMD) determines the decomposition effect to a large degree. In order to achieve the optimal selection of relevant parameters in VMD, an improved parametric adaptive VMD method based on Hybrid Grey Wolf Optimizer (HGWO) is proposed. First of all, taking the minimum local envelope entropy of modal components in the VMD as the optimization goal, HGWO algorithm is used to search for the optimal parameter combination in VMD. Then, the improved VMD is used to decompose the vibration signal to obtain modal components. For improving the stability of fault feature, the initial eigenmatrix composed of the key modal components are decomposed by Singular Value Decomposition (SVD), and the singular value is taken as the final eigenmatrix. Finally, the feature matrix is input to the Deep Belief Network (DBN) for learning and training, so as to realize the early fault diagnosis of rolling bearing. The comparative experimental analysis shows that the improved VMD method after parameter optimization can extract the early failure characteristics of rolling bearing more distinctly, and the fault diagnosis model based on this method has higher accuracy and application value.

[1]  Haidong Shao,et al.  Rolling bearing fault diagnosis using adaptive deep belief network with dual-tree complex wavelet packet. , 2017, ISA transactions.

[2]  Farhat Fnaiech,et al.  Bi-spectrum based-EMD applied to the non-stationary vibration signals for bearing faults diagnosis , 2014, 2014 6th International Conference of Soft Computing and Pattern Recognition (SoCPaR).

[3]  Gangbing Song,et al.  Health Degradation Monitoring and Early Fault Diagnosis of a Rolling Bearing Based on CEEMDAN and Improved MMSE , 2018, Materials.

[4]  Xiaolong Wang,et al.  Rolling bearing fault diagnosis based on variational mode decomposition and permutation entropy , 2016, 2016 13th International Conference on Ubiquitous Robots and Ambient Intelligence (URAI).

[5]  Yu Wang,et al.  An intelligent fault diagnosis system for process plant using a functional HAZOP and DBN integrated methodology , 2015, Eng. Appl. Artif. Intell..

[6]  Min Xie,et al.  A Dynamic-Bayesian-Network-Based Fault Diagnosis Methodology Considering Transient and Intermittent Faults , 2017, IEEE Transactions on Automation Science and Engineering.

[7]  Yong Lv,et al.  Improved Dynamic Mode Decomposition and Its Application to Fault Diagnosis of Rolling Bearing , 2018, Sensors.

[8]  Steven Verstockt,et al.  Convolutional Neural Network Based Fault Detection for Rotating Machinery , 2016 .

[9]  Norden E. Huang,et al.  Ensemble Empirical Mode Decomposition: a Noise-Assisted Data Analysis Method , 2009, Adv. Data Sci. Adapt. Anal..

[10]  Yanyang Zi,et al.  Independence-oriented VMD to identify fault feature for wheel set bearing fault diagnosis of high speed locomotive , 2017 .

[11]  Thomas Hofmann,et al.  Greedy Layer-Wise Training of Deep Networks , 2007 .

[12]  Andrew Lewis,et al.  Grey Wolf Optimizer , 2014, Adv. Eng. Softw..

[13]  Fei Wang,et al.  Natural gas pipeline small leakage feature extraction and recognition based on LMD envelope spectrum entropy and SVM , 2014 .

[14]  Daniel Dotta,et al.  Identification of electromechanical oscillatory modes based on variational mode decomposition , 2019, Electric Power Systems Research.

[15]  Reza Golafshan,et al.  SVD and Hankel matrix based de-noising approach for ball bearing fault detection and its assessment using artificial faults , 2016 .

[16]  Jérôme Gilles,et al.  Empirical Wavelet Transform , 2013, IEEE Transactions on Signal Processing.

[17]  Jay Lee,et al.  Robust performance degradation assessment methods for enhanced rolling element bearing prognostics , 2003, Adv. Eng. Informatics.

[18]  Chong Zhou,et al.  Rolling Bearing Fault Diagnosis Based on an Improved HTT Transform , 2018, Sensors.

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

[20]  Jingbo Gai,et al.  Research on Fault Diagnosis Based on Singular Value Decomposition and Fuzzy Neural Network , 2018 .

[21]  Jun Wu,et al.  Hybridizing grey wolf optimization with differential evolution for global optimization and test scheduling for 3D stacked SoC , 2015 .

[22]  Changqing Shen,et al.  A self-adaptive deep belief network with Nesterov momentum for the fault diagnosis of rolling element bearings , 2017, ICDLT '17.

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

[24]  Zhiqiang Chen,et al.  Deep neural networks-based rolling bearing fault diagnosis , 2017, Microelectron. Reliab..

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

[26]  Zhongmin Deng,et al.  An improved EMD method based on the multi-objective optimization and its application to fault feature extraction of rolling bearing , 2017 .

[27]  P. R. Ukrainetz,et al.  Frequency Domain Modelling and Identification of 2D Digital Servo Valve , 2000 .

[28]  Weihua Li,et al.  Multisensor Feature Fusion for Bearing Fault Diagnosis Using Sparse Autoencoder and Deep Belief Network , 2017, IEEE Transactions on Instrumentation and Measurement.

[29]  Yee Whye Teh,et al.  A Fast Learning Algorithm for Deep Belief Nets , 2006, Neural Computation.