Health condition identification of planetary gearboxes based on variational mode decomposition and generalized composite multi-scale symbolic dynamic entropy.

This paper proposes a novel fault diagnosis method based on variational mode decomposition (VMD) and generalized composite multi-scale symbol dynamic entropy (GCMSDE) to identify the different health conditions of planetary gearboxes. First, VMD is adopted to remove the noises and highlight the fault symptoms. Second, GCMSDE is utilized to extract the fault features from the denoised vibration signals. Third, the Laplacian score (LS) approach is employed to refine the fault features. Finally, the new features are fed into Softmax regression to identify the health conditions of planetary gearboxes. The proposed method is numerically and experimentally demonstrated to be able to differentiate seven localized fault types on the sun gear, planet gear and ring gear of planetary gearboxes.

[1]  Jeffrey M. Hausdorff,et al.  Multiscale entropy analysis of human gait dynamics. , 2003, Physica A.

[2]  Zhipeng Feng,et al.  Fault diagnosis of wind turbine planetary gearbox under nonstationary conditions via adaptive optimal kernel time–frequency analysis , 2014 .

[3]  Hee-Seok Oh,et al.  Enhancement of variational mode decomposition with missing values , 2018, Signal Process..

[4]  Shuen-De Wu,et al.  Refined scale-dependent permutation entropy to analyze systems complexity , 2016 .

[5]  Fulei Chu,et al.  Fault diagnosis for wind turbine planetary ring gear via a meshing resonance based filtering algorithm. , 2017, ISA transactions.

[6]  Jian-Jiun Ding,et al.  Bearing Fault Diagnosis Based on Multiscale Permutation Entropy and Support Vector Machine , 2012, Entropy.

[7]  L M Hively,et al.  Detecting dynamical changes in time series using the permutation entropy. , 2004, Physical review. E, Statistical, nonlinear, and soft matter physics.

[8]  Asok Ray,et al.  Symbolic time series analysis via wavelet-based partitioning , 2006 .

[9]  Yaguo Lei,et al.  Condition monitoring and fault diagnosis of planetary gearboxes: A review , 2014 .

[10]  Deng Cai,et al.  Laplacian Score for Feature Selection , 2005, NIPS.

[11]  Haiyang Pan,et al.  Rolling bearing fault detection and diagnosis based on composite multiscale fuzzy entropy and ensemble support vector machines , 2017 .

[12]  Feng Jia,et al.  An Intelligent Fault Diagnosis Method Using Unsupervised Feature Learning Towards Mechanical Big Data , 2016, IEEE Transactions on Industrial Electronics.

[13]  Yu Jiang,et al.  Multi-dimensional variational mode decomposition for bearing-crack detection in wind turbines with large driving-speed variations , 2018 .

[14]  Ming J. Zuo,et al.  Planetary Gearbox Fault diagnosis via Joint Amplitude and Frequency Demodulation Analysis Based on Variational Mode Decomposition , 2017 .

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

[16]  Junsheng Cheng,et al.  Multiscale Permutation Entropy Based Rolling Bearing Fault Diagnosis , 2014 .

[17]  C. Peng,et al.  Analysis of complex time series using refined composite multiscale entropy , 2014 .

[18]  Stphane Tuffry,et al.  Data Mining and Statistics for Decision Making , 2011 .

[19]  Xianzhi Wang,et al.  Early fault diagnosis of rolling bearings based on hierarchical symbol dynamic entropy and binary tree support vector machine , 2018, Journal of Sound and Vibration.

[20]  Junsheng Cheng,et al.  Generalized composite multiscale permutation entropy and Laplacian score based rolling bearing fault diagnosis , 2018 .

[21]  Ming J. Zuo,et al.  Dynamic modeling of gearbox faults: A review , 2018 .

[22]  Shuen-De Wu,et al.  Refined Composite Multiscale Permutation Entropy to Overcome Multiscale Permutation Entropy Length Dependence , 2015, IEEE Signal Processing Letters.

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

[24]  H. Azami,et al.  Refined composite multivariate generalized multiscale fuzzy entropy: A tool for complexity analysis of multichannel signals , 2017 .

[25]  Armin B. Cremers,et al.  Laser-based segment classification using a mixture of bag-of-words , 2013, 2013 IEEE/RSJ International Conference on Intelligent Robots and Systems.

[26]  Chun-Chieh Wang,et al.  Time Series Analysis Using Composite Multiscale Entropy , 2013, Entropy.

[27]  Ary L. Goldberger,et al.  Generalized Multiscale Entropy Analysis: Application to Quantifying the Complex Volatility of Human Heartbeat Time Series , 2015, Entropy.

[28]  Yongbo Li,et al.  A new rolling bearing fault diagnosis method based on multiscale permutation entropy and improved support vector machine based binary tree , 2016 .

[29]  Minqiang Xu,et al.  A fault diagnosis scheme for planetary gearboxes using modified multi-scale symbolic dynamic entropy and mRMR feature selection , 2017 .

[30]  Asok Ray,et al.  Estimation of slowly varying parameters in nonlinear systems via symbolic dynamic filtering , 2008, Signal Process..

[31]  Norden E. Huang,et al.  On Instantaneous Frequency , 2009, Adv. Data Sci. Adapt. Anal..

[32]  Concha Bielza,et al.  Regularized logistic regression without a penalty term: An application to cancer classification with microarray data , 2011, Expert Syst. Appl..

[33]  Jeffrey S. Mayer,et al.  Symbolic dynamic filtering of complex systems , 2007 .

[34]  Ming J. Zuo,et al.  Spur Gear Tooth Pitting Propagation Assessment Using Model-based Analysis , 2017, Chinese Journal of Mechanical Engineering.

[35]  Madalena Costa,et al.  Multiscale entropy analysis of complex physiologic time series. , 2002, Physical review letters.

[36]  Yongbo Li,et al.  Health Condition Monitoring and Early Fault Diagnosis of Bearings Using SDF and Intrinsic Characteristic-Scale Decomposition , 2016, IEEE Transactions on Instrumentation and Measurement.

[37]  Minqiang Xu,et al.  An improvement EMD method based on the optimized rational Hermite interpolation approach and its application to gear fault diagnosis , 2015 .

[38]  Junsheng Cheng,et al.  A rolling bearing fault diagnosis method based on multi-scale fuzzy entropy and variable predictive model-based class discrimination , 2014 .

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

[40]  Xin Jin,et al.  Symbolic Dynamic Filtering and Language Measure for Behavior Identification of Mobile Robots , 2012, IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics).

[41]  Tao Liu,et al.  A Refined Composite Multivariate Multiscale Fuzzy Entropy and Laplacian Score-Based Fault Diagnosis Method for Rolling Bearings , 2017, Entropy.

[42]  Minqiang Xu,et al.  A fault diagnosis scheme for planetary gearboxes using adaptive multi-scale morphology filter and modified hierarchical permutation entropy , 2018 .

[43]  J. Richman,et al.  Physiological time-series analysis using approximate entropy and sample entropy. , 2000, American journal of physiology. Heart and circulatory physiology.