Current Science

Open Access Open Access  Restricted Access Subscription Access

A Feature Extraction Method for Shearer Cutting Pattern Recognition Based on Improved Local Mean Decomposition and Multi-Scale Fuzzy Entropy


Affiliations
1 School of Mechatronic Engineering, China University of Mining and Technology, Xuzhou 221116, China
 

Aiming at accurately identifying shearer cutting patterns, this article proposes a new feature extraction method based on improved local mean decomposition (LMD) and multi-scale fuzzy entropy (MFE). The cubic trigonometric Hermite interpolation was used to calculate local mean and envelope estimate functions to improve LMD decomposition results and a sum of product functions was acquired. Furthermore, MFE, referring to the calculation of fuzzy entropy over a range of scales, was designed to measure the complexity and self-similarity of vibration signals and extract the features from the decomposition results. Subsequently, the obtained feature vectors were fed into two classifiers of support vector machine and back propagation neutral network to realize the cutting pattern recognition. The experimental results indicate the applicability and effectiveness of the methodology and demonstrate that the proposed algorithm could perform better in identifying different cutting categories of shearer.

Keywords

Feature Extraction, Local Mean Decomposition, Multi-Scale Fuzzy Entropy, Shearer Cutting Pattern.
User
Notifications
Font Size

  • Feng, Z. and Zuo, M. J., Vibration signal models for fault diagnosis of planetary gearboxes. J. Sound Vib., 2012, 331, 4919–4939.

  • Zvokelj, M., Zupan, S. and Prebil, I., EEMD-based multiscale ICA method for slewing bearing fault detection and diagnosis. J. Sound Vib., 2016, 370, 394–423.

  • Lee, J. H., Kim, J. and Kim, H. J., Development of enhanced Wigner–Ville distribution function. Mech. Syst. Signal Process., 2001, 13, 367–398.

  • Lin, J. and Qu, L. S., Feature extraction based on Morlet wavelet and its application for mechanical fault diagnosis. J. Sound Vib., 2000, 234, 135–148.

  • Sanz, J., Perera, R. and Huerta, C., Fault diagnosis of rotating machinery based on auto-associative neural networks and wavelet transforms. J. Sound Vib., 2007, 302, 981–999.

  • Fu, Y. B., Chui, C. K. and Teo, C. L., Accurate two-dimensional cardiac strain calculation using adaptive windowed Fourier transform and Gabor wavelet transform. Int. J. Comput. Assist. Radiol., 2013, 8, 135–144.

  • Hasson, M., Wavelet-based filters for accurate computation of derivatives. Math. Comput., 2005, 75, 259–280.

  • Heidari, M., Homaei, H., Golestanian, H. and Heidari, A., Fault diagnosis of gearboxes using wavelet support vector machine, least square support vector machine and wavelet packet transform. J. Vibroeng., 2016, 18, 860–875.

  • Huang, N. E. et al., The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis. Proc. R. Soc. London, Ser. A, 1998, 454, 903–995.

  • Smith, J. S., The local mean decomposition and its application to EEG perception data. J. R. Soc. Interface, 2005, 2, 443–454.

  • Do, V. T. and Nguyen, L. C., Adaptive empirical mode decomposition for bearing fault detection. Stroj. Vestn. – J. Mech. E., 2016, 62, 281–290.

  • Wei, L. Y., A hybrid ANFIS model based on empirical mode decomposition for stock time series forecasting. Appl. Soft Comput., 2016, 42, 368–376.

  • Wu, X., Liu, T. B., Liu, R. and Zhao, L., Surge detection methods using empirical mode decomposition and continuous wavelet transform for a centrifugal compressor. J. Mech. Sci. Technol., 2016, 30, 1533–1536.

  • Liu, H. H. and Han, M. H., A fault diagnosis method based on local mean decomposition and multi-scale entropy for roller bearings. Mech. Mach. Theory, 2014, 75, 67–78.

  • Li, Y. B., Xu, M. Q., Wang, R. X. and Huang, W. H., A fault diagnosis scheme for rolling bearing based on local mean decomposition and improved multiscale fuzzy entropy. J. Sound Vib., 2016, 360, 277–299.

  • Cheng, J. S., Zhang, K. and Yang, Y., An order tracking technique for the gear fault diagnosis using local mean decomposition method. Mech. Mach. Theory, 2012, 55, 67–76.

  • Cheng, J. S., Zhang, K. and Yang, Y., Local mean decomposition method and its application to roller bearing fault diagnosis. China Mech. Eng., 2009, 20, 2711–2717.

  • Peng, Z. K., Tse, P. W. and Chu, F. L., An improved Hilbert–Huang transform and its application in vibration signal analysis. J. Sound Vib., 2005, 286, 187–205.

  • Li, Y. B., Xu, M. Q., Wei, Y. and Huang, W. H., An improvement EMD method based on the optimized rational Hermite interpolation approach and its application to gear fault diagnosis. Measurement, 2015, 63, 330–345.

  • Hu, J. S., Yang, S. X. and Ren, D. Q., Spline-based local mean decomposition method for vibration signal. J. Data Acquisition Process., 2009, 24, 82–86.

  • Wang, M. D., Zhang, L. B. and Liang, W., Local mean decomposition method based on B-spline interpolation. J. Vib. Shock, 2010, 29, 73–78.

  • Deng, L. F. and Zhao, R. Z., Rotor vibration signal analysis based on the CHI-LMD method. J. Vib. Shock, 2014, 33, 58–64.

  • Li, H. L., Guo, L. H., Chen, T., Yang, L. M. and Wang, X. S., Iris recognition based on PCHIP–LMD. Opt. Precis. Eng., 2013, 21, 197–206.

  • Zhang, K., Cheng, J. S. and Yang, Y., The local mean decomposition method based on rational spline and its application. J. Vib. Eng., 2011, 24, 96–103.

  • Xie, J., Tan, J. Q. and Li, S. F., Rational cubic Hermite interpolating apline and its approximation properties. Chin. J. Eng. Math., 2011, 28, 385–392.

  • Li, Y. B., Xu, M. Q. Zhao, H. Y., Yu, W. and Huang, W. H., A new rotating machinery fault diagnosis method based on improved local mean decomposition. Digit. Signal Process., 20115, 46, 201–214.

  • Zhao, H. Y., Xu, M. Q. and Wang, J. D., Local mean decomposition based on rational Hermite interpolation and its application for fault diagnosis of reciprocating compressor. J. Mech. Eng., 2015, 51, 83–89.

  • Li, J. C., Zhong, Y. E. and Xie, C., Cubic trigonometric Hermite interpolating splines curves with shape parameters. Comput. Eng. Appl., 2014, 50, 182–185.

  • Pincus, S. M., Approximate entropy as a measure of system complexity. Proc. Natl. Acad. Sci. USA, 1991, 88, 2297–2301.

  • Richman, J. S. and Moorman, J. R., Physiological time-series analysis using approximate entropy and sample entropy. Am. J. Physiol.–Heart Circ. Phys., 2000, 278, H2039–H2049.

  • Costa, M., Goldberger, A. L. and Peng, C. K., Multiscale entropy analysis of complex physiologic time series. Phys. Rev. Lett., 2002, 89, 068102.

  • Costa, M., Goldberger, A. L. and Peng, C. K., Multiscale entropy analysis of biological signals. Phys. Rev. Lett., 2005, 71, 021906.

  • Zhang, L., Xiong, G. and Liu, H., Bearing fault diagnosis using multi-scale entropy and adaptive neuro-fuzzy inference. Expert Syst. Appl., 2010, 37, 6077–6085.

  • Zheng, J. D., Cheng, J. S. and Yang, Y., A rolling bearing fault diagnosis approach based on multiscale entropy. J. Hunan University (Natural Sciences), 2012, 39, 38–41.

  • Chen, W., Zhuang, J., Yu, W. and Wang, Z., Measuring complexity using FuzzyEn, ApEn, and SampEn. Med. Eng. Phys., 2009, 31, 61–68.

  • Zheng, J. D., Cheng, J. S., Yang, Y. and Luo, S. R., A rolling bearing fault diagnosis method based on multi-scale fuzzy entropy and variable predictive model-based class discrimination. Mech. Mach. Theory, 2014, 78, 187–200.

  • Zheng, J. D., Chen, M. J., Cheng, J. S. and Yang, Y., Multiscale fuzzy entropy and its application in rolling bearing fault diagnosis. J. Vib. Eng., 2014, 27, 145–151.

  • Wu, J. D. and Tsai, Y. J., Speaker identification system using empirical mode decomposition and an artificial neural network. Expert Syst. Appl., 2011, 38, 6112–6117.

  • Polat, K. and Güneş, S., An expert system approach based on principal component analysis and adaptive neuro-fuzzy inference system to diagnosis of diabetes disease. Digit. Signal Process., 2007, 17, 702–710.

  • Si, L., Wang, Z. B., Liu, X. H., Tan, C., Liu, Z. and Xu, J., Identification of shearer cutting patterns using vibration signals based on a least squares support vector machine with an improved fruit fly optimization algorithm. Sensors, 2016, 16, 90.

  • Yentes, J. M., Hunt, N. and Schmid, K. K., The appropriate use of approximate entropy and sample entropy with short data sets. Ann. Biomed. Eng., 2013, 41, 349–365.

  • He, X., Cai, D. and Niyogi, P., Laplacian score for feature selection. Adv. Neural Inf. Process. Syst., 2005, 507–551.


Abstract Views: 407

PDF Views: 137




  • A Feature Extraction Method for Shearer Cutting Pattern Recognition Based on Improved Local Mean Decomposition and Multi-Scale Fuzzy Entropy

Abstract Views: 407  |  PDF Views: 137

Authors

Lei Si
School of Mechatronic Engineering, China University of Mining and Technology, Xuzhou 221116, China
Zhongbin Wang
School of Mechatronic Engineering, China University of Mining and Technology, Xuzhou 221116, China
Chao Tan
School of Mechatronic Engineering, China University of Mining and Technology, Xuzhou 221116, China
Xinhua Liu
School of Mechatronic Engineering, China University of Mining and Technology, Xuzhou 221116, China
Xihua Xu
School of Mechatronic Engineering, China University of Mining and Technology, Xuzhou 221116, China

Abstract


Aiming at accurately identifying shearer cutting patterns, this article proposes a new feature extraction method based on improved local mean decomposition (LMD) and multi-scale fuzzy entropy (MFE). The cubic trigonometric Hermite interpolation was used to calculate local mean and envelope estimate functions to improve LMD decomposition results and a sum of product functions was acquired. Furthermore, MFE, referring to the calculation of fuzzy entropy over a range of scales, was designed to measure the complexity and self-similarity of vibration signals and extract the features from the decomposition results. Subsequently, the obtained feature vectors were fed into two classifiers of support vector machine and back propagation neutral network to realize the cutting pattern recognition. The experimental results indicate the applicability and effectiveness of the methodology and demonstrate that the proposed algorithm could perform better in identifying different cutting categories of shearer.

Keywords


Feature Extraction, Local Mean Decomposition, Multi-Scale Fuzzy Entropy, Shearer Cutting Pattern.

References





DOI: https://doi.org/10.18520/cs%2Fv112%2Fi11%2F2243-2252