Ensemble empirical mode decomposition (EEMD) and Teager-Kaiser energy operator (TKEO) based damage identification of roller bearings using one-class support vector machine.

Ensemble empirical mode decomposition (EEMD) is a newly developed noise assisted method aimed to solve mode mixing problem exists in empirical mode decomposition (EMD) method. Although EEMD has been utilized in various applications successfully, small defects of bearings are not able to be detected, especially in automatic defect detection, when only healthy samples are available for training. Teager-Kaiser energy operator (TKEO) technique is a non-linear operator that can track the energy and identify the instantaneous frequencies and instantaneous amplitudes of signals at any instant. As Teager-Kaiser energy operator (TKEO) technique detects a sudden change of the energy stream without any priori assumption of the data structure, it can be utilized for vibration based condition monitoring (non-stationary signals). In this study it is investigated whether an automatic method is able to diagnose a small defect level of roller bearings through processing of the acquired signals. After applying TKEO on IMFs decomposed by means of EEMD, the extracted informative feature vectors of the healthy bearing are used to construct the separating hyperplane using one-class support vector machine (SVM). Then, success rates of state identification of both samples (healthy and faulty) are examined by labelling the samples. The data were generated by means of a test rig assembled in the labs of the Dynamics & Identification Research Group (DIRG) at mechanical and aerospace engineering department, Politecnico di Torino. Various operating conditions (three shaft speeds, three external loads and one small size damage on a roller) were considered to obtain reliable results.

[1]  Petros Maragos,et al.  On amplitude and frequency demodulation using energy operators , 1993, IEEE Trans. Signal Process..

[2]  Petros Maragos,et al.  Energy separation in signal modulations with application to speech analysis , 1993, IEEE Trans. Signal Process..

[3]  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.

[4]  Miguel A. Ferrer,et al.  Application of the Teager-Kaiser energy operator in bearing fault diagnosis. , 2013, ISA transactions.

[5]  Shaoze Yan,et al.  Teager Energy Spectrum for Fault Diagnosis of Rolling Element Bearings , 2011 .

[6]  J. F. Kaiser,et al.  On a simple algorithm to calculate the 'energy' of a signal , 1990, International Conference on Acoustics, Speech, and Signal Processing.

[7]  Liwei Tang,et al.  Bearing Fault Detection and Diagnosis Based on Teager-Huang Transform , 2009, Int. J. Wavelets Multiresolution Inf. Process..

[8]  Bong-Hwan Koh,et al.  Fault Detection of Roller-Bearings Using Signal Processing and Optimization Algorithms , 2014, Sensors.

[9]  H. Teager Some observations on oral air flow during phonation , 1980 .

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

[11]  Hyun Joon Shin,et al.  One-class support vector machines - an application in machine fault detection and classification , 2005, Comput. Ind. Eng..

[12]  Jing Wang,et al.  Rolling Bearing Fault Detection Based on the Teager Energy Operator and Elman Neural Network , 2013 .

[13]  Hui Li,et al.  Bearing Faults Diagnosis Based on Teager Energy Operator Demodulation Technique , 2009, 2009 International Conference on Measuring Technology and Mechatronics Automation.

[14]  Bernhard Schölkopf,et al.  Support Vector Method for Novelty Detection , 1999, NIPS.

[15]  Vladimir N. Vapnik,et al.  The Nature of Statistical Learning Theory , 2000, Statistics for Engineering and Information Science.

[16]  Yang Yu,et al.  The application of energy operator demodulation approach based on EMD in machinery fault diagnosis , 2007 .