A gearbox fault diagnosis method based on frequency-modulated empirical mode decomposition and support vector machine

During the operation process of a gearbox, the vibration signals can reflect the dynamic states of the gearbox. The feature extraction of the vibration signal will directly influence the accuracy and effectiveness of fault diagnosis. One major challenge associated with the extraction process is the mode mixing, especially under such circumstance of intensive frequency. A novel fault diagnosis method based on frequency-modulated empirical mode decomposition is proposed in this paper. Firstly, several stationary intrinsic mode functions can be obtained after the initial vibration signal is processed using frequency-modulated empirical mode decomposition method. Using the method, the vibration signal feature can be extracted in unworkable region of the empirical mode decomposition. The method has the ability to separate such close frequency components, which overcomes the major drawback of the conventional methods. Numerical simulation results showed the validity of the developed signal processing method. Secondly, energy entropy was calculated to reflect the changes in vibration signals in relation to faults. At last, the energy distribution could serve as eigenvector of support vector machine to recognize the dynamic state and fault type of the gearbox. The analysis results from the gearbox signals demonstrate the effectiveness and veracity of the diagnosis approach.

[1]  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.

[2]  Peter W. Tse,et al.  Wavelet Analysis and Envelope Detection For Rolling Element Bearing Fault Diagnosis—Their Effectiveness and Flexibilities , 2001 .

[3]  Gabriel Rilling,et al.  Empirical mode decomposition as a filter bank , 2004, IEEE Signal Processing Letters.

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

[5]  Gabriel Rilling,et al.  Bivariate Empirical Mode Decomposition , 2007, IEEE Signal Processing Letters.

[6]  Toshihisa Tanaka,et al.  Complex Empirical Mode Decomposition , 2007, IEEE Signal Processing Letters.

[7]  Gabriel Rilling,et al.  One or Two Frequencies? The Empirical Mode Decomposition Answers , 2008, IEEE Transactions on Signal Processing.

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

[9]  Norden E. Huang,et al.  The Multi-Dimensional Ensemble Empirical Mode Decomposition Method , 2009, Adv. Data Sci. Adapt. Anal..

[10]  Xin Zhang,et al.  Frequency modulated empirical mode decomposition method for the identification of instantaneous modal parameters of aeroelastic systems , 2012 .

[11]  Chrysostomos D. Stylios,et al.  Bearing fault detection based on hybrid ensemble detector and empirical mode decomposition , 2013 .

[12]  Xiaoyuan Zhang,et al.  Multi-fault diagnosis for rolling element bearings based on ensemble empirical mode decomposition and optimized support vector machines , 2013 .

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

[14]  Adam Glowacz,et al.  Diagnostics of DC and Induction Motors Based on the Analysis of Acoustic Signals , 2014 .

[15]  Adam Glowacz,et al.  Diagnostics of Synchronous Motor Based on Analysis of Acoustic Signals with the use of Line Spectral Frequencies and K-nearest Neighbor Classifier , 2015 .

[16]  Xiaoming Xue,et al.  An adaptively fast ensemble empirical mode decomposition method and its applications to rolling element bearing fault diagnosis , 2015 .

[17]  Rusinek Rafal,et al.  Chatter identification methods on the basis of time series measured during titanium superalloy milling , 2015 .

[18]  U. Rajendra Acharya,et al.  Application of Entropy Measures on Intrinsic Mode Functions for the Automated Identification of Focal Electroencephalogram Signals , 2015, Entropy.

[19]  Hong-Tsu Young,et al.  High-Speed Spindle Fault Diagnosis with the Empirical Mode Decomposition and Multiscale Entropy Method , 2015, Entropy.

[20]  Adam Glowacz,et al.  Recognition of Acoustic Signals of Synchronous Motors with the Use of MoFS and Selected Classifiers , 2015 .

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

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