A fault diagnosis scheme for planetary gearboxes using adaptive multi-scale morphology filter and modified hierarchical permutation entropy

Abstract The fault diagnosis of planetary gearboxes is crucial to reduce the maintenance costs and economic losses. This paper proposes a novel fault diagnosis method based on adaptive multi-scale morphological filter (AMMF) and modified hierarchical permutation entropy (MHPE) to identify the different health conditions of planetary gearboxes. In this method, AMMF is firstly adopted to remove the fault-unrelated components and enhance the fault characteristics. Second, MHPE is utilized to extract the fault features from the denoised vibration signals. Third, Laplacian score (LS) approach is employed to refine the fault features. In the end, the obtained features are fed into the binary tree support vector machine (BT-SVM) to accomplish the fault pattern identification. The proposed method is numerically and experimentally demonstrated to be able to recognize the different fault categories of planetary gearboxes.

[1]  Changqing Shen,et al.  A fast and adaptive varying-scale morphological analysis method for rolling element bearing fault diagnosis , 2013 .

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

[3]  Minqiang Xu,et al.  A fault diagnosis scheme for rolling bearing based on local mean decomposition and improved multiscale fuzzy entropy , 2016 .

[4]  Ming J. Zuo,et al.  Analytically evaluating the influence of crack on the mesh stiffness of a planetary gear set , 2014 .

[5]  Guoyan Li,et al.  Fault Diagnosis for a Multistage Planetary Gear Set Using Model-Based Simulation and Experimental Investigation , 2016 .

[6]  Ming Liang,et al.  Time–frequency analysis based on Vold-Kalman filter and higher order energy separation for fault diagnosis of wind turbine planetary gearbox under nonstationary conditions , 2016 .

[7]  Yitao Liang,et al.  A novel bearing fault diagnosis model integrated permutation entropy, ensemble empirical mode decomposition and optimized SVM , 2015 .

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

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

[10]  Badong Chen,et al.  Weighted-permutation entropy: a complexity measure for time series incorporating amplitude information. , 2013, Physical review. E, Statistical, nonlinear, and soft matter physics.

[11]  Long Zhang,et al.  Bearing fault diagnosis using multi-scale entropy and adaptive neuro-fuzzy inference , 2010, Expert Syst. Appl..

[12]  Lijun Zhang,et al.  Multiscale morphology analysis and its application to fault diagnosis , 2008 .

[13]  Xiaofei Zhang,et al.  Crack Level Estimation Approach for Planetary Gear Sets Based on Simulation Signal and GRA , 2012 .

[14]  Keheng Zhu,et al.  A roller bearing fault diagnosis method based on hierarchical entropy and support vector machine with particle swarm optimization algorithm , 2014 .

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

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

[17]  Ioannis Antoniadis,et al.  APPLICATION OF MORPHOLOGICAL OPERATORS AS ENVELOPE EXTRACTORS FOR IMPULSIVE-TYPE PERIODIC SIGNALS , 2003 .

[18]  Petros Maragos,et al.  Pattern Spectrum and Multiscale Shape Representation , 1989, IEEE Trans. Pattern Anal. Mach. Intell..

[19]  Yaguo Lei,et al.  Health condition identification of multi-stage planetary gearboxes using a mRVM-based method , 2015 .

[20]  Ming J. Zuo,et al.  Joint amplitude and frequency demodulation analysis based on local mean decomposition for fault diagnosis of planetary gearboxes , 2013 .

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

[22]  Jianbo Yu,et al.  Local and Nonlocal Preserving Projection for Bearing Defect Classification and Performance Assessment , 2012, IEEE Transactions on Industrial Electronics.

[23]  J. Serra,et al.  An overview of morphological filtering , 1992 .

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

[25]  Ming J. Zuo,et al.  Diagonal slice spectrum assisted optimal scale morphological filter for rolling element bearing fault diagnosis , 2017 .

[26]  Bing Li,et al.  Gear fault detection using multi-scale morphological filters , 2011 .

[27]  K. I. Ramachandran,et al.  Fault diagnosis of spur bevel gear box using discrete wavelet features and Decision Tree classification , 2009, Expert Syst. Appl..

[28]  Ming J. Zuo,et al.  Feature selection for damage degree classification of planetary gearboxes using support vector machine , 2011 .

[29]  Hao Tian,et al.  A new feature extraction and selection scheme for hybrid fault diagnosis of gearbox , 2011, Expert Syst. Appl..

[30]  Jian Xiao,et al.  Multifault Diagnosis for Rolling Element Bearings Based on Intrinsic Mode Permutation Entropy and Ensemble Optimal Extreme Learning Machine , 2014 .

[31]  Xiaomin Zhao,et al.  Feature selection for fault level diagnosis of planetary gearboxes , 2014, Adv. Data Anal. Classif..

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

[33]  Yang Li,et al.  Demodulation for hydraulic pump fault signals based on local mean decomposition and improved adaptive multiscale morphology analysis , 2015 .

[34]  Ruqiang Yan,et al.  Permutation entropy: A nonlinear statistical measure for status characterization of rotary machines , 2012 .

[35]  Ying Jiang,et al.  Hierarchical entropy analysis for biological signals , 2011, J. Comput. Appl. Math..

[36]  Aijun Hu,et al.  Selection principle of mathematical morphological operators in vibration signal processing , 2016 .

[37]  Zhipeng Feng,et al.  Fault diagnosis for wind turbine planetary gearboxes via demodulation analysis based on ensemble empirical mode decomposition and energy separation , 2012 .

[38]  Robert B. Randall,et al.  Application of spectral kurtosis for detection of a tooth crack in the planetary gear of a wind turbine , 2009 .

[39]  Jonathan A. Keller,et al.  Detection of a fatigue crack in a UH-60A planet gear carrier using vibration analysis , 2006 .

[40]  Darryll J. Pines,et al.  A review of vibration-based techniques for helicopter transmission diagnostics , 2005 .

[41]  Jean Serra,et al.  Image Analysis and Mathematical Morphology , 1983 .