Cutting Pattern Identification for Coal Mining Shearer through Sound Signals Based on a Convolutional Neural Network
Abstract
:1. Introduction
2. Literature Review
2.1. Hilbert–Huang Transform
2.2. Convolutional Neural Network
2.3. Discussion
3. Basic Theories
3.1. Hilbert–Huang Transform
3.2. Convolutional Neural Network
4. The Proposed Method
5. Simulation and Application
5.1. Cutting Sound Acquisition
5.2. Sound Decomposition
5.3. CNN Training and Testing
6. Conclusions and Future Work
Author Contributions
Funding
Conflicts of Interest
References
- Xu, J.L.; Wang, Z.C.; Zhang, W.Z.; He, Y.P. Coal-rock interface recognition based on MFCC and neural network. Int. J. Signal Process. Image Process. Pattern Recognit. 2013, 6, 191–200. [Google Scholar]
- Bessinger, S.L.; Neison, M.G. Remnant roof coal thickness measurement with passive gamma ray instruments in coal mine. IEEE Trans. Ind. Appl. 1993, 29, 562–565. [Google Scholar] [CrossRef]
- Dong, Y.F.; Du, H.G.; Ren, W.J.; Du, Y.M. Experimental Research on Infrared Information Varying with Stress. J. Liaoning Technol. Univ. (Nat. Sci. Ed.) 2001, 20, 495–496. [Google Scholar]
- Huang, S.J.; Liu, J.G. Research of coal-rock recognition technology based on GMM clustering analysis. J. China Coal Soc. 2015, 40, 576–582. [Google Scholar]
- Wang, B.P.; Wang, Z.C.; Zhang, W.Z. Coal-rock interface recognition method based on EMD and neural network. J. Vib. Meas. Diagn. 2012, 32, 586–590. [Google Scholar] [CrossRef] [PubMed]
- Yao, Y.; Wang, H.; Li, S.; Liu, Z.; Gui, G.; Dan, Y.; Hu, J. End-To-End Convolutional Neural Network Model for Gear Fault Diagnosis Based on Sound Signals. Appl. Sci. 2018, 8, 1584. [Google Scholar] [CrossRef]
- Glowacz, A. Acoustic-Based Fault Diagnosis of Commutator Motor. Electronics 2018, 11, 299. [Google Scholar] [CrossRef]
- Vaimann, T.; Sobra, J.; Belahcen, A.; Rassolkin, A.; Rolak, M.; Kallaste, A. Induction machine fault detection using smartphone recorded audible noise. IET Sci. Meas. Technol. 2018, 12, 554–560. [Google Scholar] [CrossRef]
- Nanda, M.A.; Seminar, K.B.; Nandika, D.; Maddu, A. A Comparison Study of Kernel Functions in the Support Vector Machine and Its Application for Termite Detection. Information 2018, 9, 5. [Google Scholar] [CrossRef]
- Vununu, C.; Moon, K.-S.; Lee, S.-H.; Kwon, K.-R. A Deep Feature Learning Method for Drill Bits Monitoring Using the Spectral Analysis of the Acoustic Signals. Sensors 2018, 18, 2634. [Google Scholar] [CrossRef]
- Loh, C.R.; Wu, T.C.; Huang, N.E. Application of the empirical mode decomposition-Hilbert spectrum method to identify near-fault ground-motion characteristics and structural responses. Bull. Seismol. Soc. Am. 2001, 91, 1339–1357. [Google Scholar] [CrossRef]
- Boudraa, A.O.; Cexus, J.C. EMD-based signal filtering. IEEE Trans. Instrum. Meas. 2007, 56, 2196–2202. [Google Scholar] [CrossRef]
- Xuan, B.; Xie, Q.W.; Peng, S.L. EMD sifting based on bandwidth. IEEE Signal Process. Lett. 2007, 14, 537–541. [Google Scholar] [CrossRef]
- Huang, N.E.; Shen, Z.; Long, S.R. The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis. In Proceedings of the Royal Society of London A; The Royal Society: London, UK, 1998. [Google Scholar]
- Huang, N.E.; Wu, Z.H. A review on Hilbert-Huang transform: Method and its applications to geophysical studies. Rev. Geophys. 2008, 46. [Google Scholar] [CrossRef] [Green Version]
- Su, Z.Y.; Zhang, Y.M.; Jia, M.P.; Xu, F.Y.; Hu, J.Z. Gear fault identification and classification of singular value decomposition based on Hilbert-Huang transform. J. Mech. Sci. Technol. 2011, 25, 267–272. [Google Scholar] [CrossRef]
- Tychkov, A.Y.; Alimuradov, A.K.; Churakov, P.P. Adaptive Signal Processing Method for Speech Organ Diagnostics. Meas. Tech. 2016, 59, 485–490. [Google Scholar] [CrossRef]
- Li, H.; Xue, G.Q.; Zhao, P.; Zhong, H.S.; Khan, M.Y. The Hilbert-Huang Transform-Based Denoising Method for the TEM Response of a PRBS Source Signal. Pure Appl. Geophys. 2016, 173, 2777–2789. [Google Scholar]
- Hamdi, S.E.; Le Duff, A.; Simon, L.; Plantier, G.; Sourice, A.; Feuilloy, M. Acoustic emission pattern recognition approach based on Hilbert-Huang transform for structural health monitoring in polymer-composite materials. Appl. Acoust. 2013, 74, 746–757. [Google Scholar] [CrossRef]
- Kurbatskii, V.G.; Sidorov, D.N.; Spiryaev, V.A.; Tomin, N.V. On the Neural Network Approach for Forecasting of Nonstationary Time Series on the Basis of the Hilbert-Huang Transform. Autom. Remote Control 2011, 72, 1405–1414. [Google Scholar] [CrossRef]
- Guido, R.C. A tutorial review on entropy-based handcrafted feature extraction for information fusion. Inf. Fusion 2018, 41, 161–175. [Google Scholar] [CrossRef]
- Glowacz, A. Fault diagnosis of single-phase induction motor based on acoustic signals. Mech. Syst. Signal Process. 2019, 117, 65–80. [Google Scholar] [CrossRef]
- Nanni, L.; Costa, Y.M.G.; Lucio, D.R.; Silla, C.N.; Brahnam, S. Combining visual and acoustic features for audio classification tasks. Pattern Recognit. Lett. 2017, 88, 49–56. [Google Scholar] [CrossRef]
- Dennis, J.; Tran, H.D.; Li, H. Spectrogram Image Feature for Sound Event Classification in Mismatched Conditions. IEEE Signal Process. Lett. 2011, 18, 130–133. [Google Scholar] [CrossRef]
- Sohaib, M.; Kim, C.-H.; Kim, J.-M. A Hybrid Feature Model and Deep-Learning-Based Bearing Fault Diagnosis. Sensors 2017, 17, 2876. [Google Scholar] [CrossRef] [PubMed]
- Khawaldeh, S.; Pervaiz, U.; Rafiq, A.; Alkhawaldeh, R.S. Noninvasive Grading of Glioma Tumor Using Magnetic Resonance Imaging with Convolutional Neural Networks. Appl. Sci. 2018, 8, 27. [Google Scholar] [CrossRef]
- Hu, H.L.; Zhang, J.; Dong, J.; Luo, Z.Y.; Xu, T.M. Identification of gas-solid two-phase flow regimes using Hilbert-Huang transform and neural-network techniques. Instrum. Sci. Technol. 2011, 39, 198–210. [Google Scholar] [CrossRef]
- Wang, Y.S.; Ma, Q.H.; Zhu, Q.; Liu, X.T.; Zhao, L.H. An intelligent approach for engine fault diagnosis based on Hilbert–Huang transform and support vector machine. Appl. Acoust. 2014, 75, 1–9. [Google Scholar] [CrossRef]
- He, K.F.; Zhang, Z.J.; Xiao, S.W.; Li, X.J. Feature extraction of AC square wave SAW arc characteristics using improved Hilbert–Huang transformation and energy entropy. Measurement 2013, 46, 1385–1392. [Google Scholar] [CrossRef]
- Peng, Z.K.; Peter, W.T.; Chu, F.L. An improved Hilbert–Huang transform and its application in vibration signal analysis. J. Sound Vib. 2005, 286, 187–205. [Google Scholar] [CrossRef]
- Yi, C.; Lin, J.; Zhang, W.; Ding, J. Faults Diagnostics of Railway Axle Bearings Based on IMF’s Confidence Index Algorithm for Ensemble EMD. Sensors 2015, 15, 10991–11011. [Google Scholar] [CrossRef]
- Fukushima, K.; Miyake, S. Neocognitron: A new algorithm for pattern recognition tolerant of deformations and shifts in position. Pattern Recognit. 1982, 15, 455–469. [Google Scholar] [CrossRef]
- LeCun, Y. A learning scheme for asymmetric threshold networks. In Proceedings of the Cognitiva’85, Paris, France, 4–7 June 1985; pp. 599–604. [Google Scholar]
- LeCun, Y.; Bottou, L.; Bengio, Y.; Haffner, P. Gradient-Based Learning Applied to Document Recognition. Proc. IEEE 1998, 86, 2278–2324. [Google Scholar] [CrossRef]
- Lauer, F.; Suen, C.Y.; Bloch, G. A trainable feature extractor for handwritten digit recognition. Pattern Recognit. 2007, 40, 1816–1824. [Google Scholar] [CrossRef] [Green Version]
- Niu, X.X.; Suen, C.Y. A novel hybrid CNN–SVM classifier for recognizing handwritten digits. Pattern Recognit. 2012, 45, 1318–1325. [Google Scholar] [CrossRef]
- Garcia, C.; Delakis, M. Convolutional face finder: A neural architecture for fast and robust face detection. IEEE Trans. Pattern Anal. Mach. Intell. 2004, 26, 1408–1423. [Google Scholar] [CrossRef] [PubMed]
- Chen, Z.Q.; Li, C.; Sanchez, R.V. Gearbox Fault Identification and Classification with Convolutional Neural Networks. Shock Vib. 2015, 2015. [Google Scholar] [CrossRef]
- Ossama, A.H.; Abdel-rahman, M.; Hui, J.; Li, D.; Gerald, P.; Dong, Y. Convolutional Neural Networks for Speech Recognition. IEEE Trans. Audio Speech Lang. Process. 2014, 22, 1533–1545. [Google Scholar]
- Swietojanski, P.; Ghoshal, A.; Renals, S. Convolutional Neural Networks for Distant Speech Recognition. IEEE Signal Process. Lett. 2014, 21, 1120–1124. [Google Scholar]
© 2018 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).
Share and Cite
Xu, J.; Wang, Z.; Tan, C.; Lu, D.; Wu, B.; Su, Z.; Tang, Y. Cutting Pattern Identification for Coal Mining Shearer through Sound Signals Based on a Convolutional Neural Network. Symmetry 2018, 10, 736. https://doi.org/10.3390/sym10120736
Xu J, Wang Z, Tan C, Lu D, Wu B, Su Z, Tang Y. Cutting Pattern Identification for Coal Mining Shearer through Sound Signals Based on a Convolutional Neural Network. Symmetry. 2018; 10(12):736. https://doi.org/10.3390/sym10120736
Chicago/Turabian StyleXu, Jing, Zhongbin Wang, Chao Tan, Daohua Lu, Baigong Wu, Zhen Su, and Yanbing Tang. 2018. "Cutting Pattern Identification for Coal Mining Shearer through Sound Signals Based on a Convolutional Neural Network" Symmetry 10, no. 12: 736. https://doi.org/10.3390/sym10120736
APA StyleXu, J., Wang, Z., Tan, C., Lu, D., Wu, B., Su, Z., & Tang, Y. (2018). Cutting Pattern Identification for Coal Mining Shearer through Sound Signals Based on a Convolutional Neural Network. Symmetry, 10(12), 736. https://doi.org/10.3390/sym10120736