Open Access
MATEC Web Conf.
Volume 255, 2019
Engineering Application of Artificial Intelligence Conference 2018 (EAAIC 2018)
Article Number 06002
Number of page(s) 7
Section Health Monitoring and Diagnosis
Published online 16 January 2019
  1. Shao H, Jiang H, Zhang H, Duan W, Liang T, Wu S. Rolling bearing fault feature learning using improved convolutional deep belief network with compressed sensing. Mech Syst Signal Process 2018;100:743–65. doi:10.1016/j.ymssp.2017.08.002. [CrossRef] [Google Scholar]
  2. Janssens O, Slavkovikj V, Vervisch B, Stockman K, Loccufier M, Verstockt S, et al. Convolutional Neural Network Based Fault Detection for Rotating Machinery. J Sound Vib 2016;377:331–45. doi:10.1016/j.jsv.2016.05.027. [Google Scholar]
  3. Gan M, Wang C, Zhu C. Construction of hierarchical diagnosis network based on deep learning and its application in the fault pattern recognition of rolling element bearings. Mech Syst Signal Process 2016;72-73:92–104. doi:10.1016/j.ymssp.2015.11.014. [CrossRef] [Google Scholar]
  4. Shao H, Jiang H, Zhang X, Niu M. Rolling bearing fault diagnosis using an optimization deep belief network. Meas Sci Technol 2015;26. doi:10.1088/0957-0233/26/11/115002. [Google Scholar]
  5. Verma NK, Gupta VK, Sharma M, Sevakula RK. Intelligent condition based monitoring of rotating machines using sparse auto-encoders. Progn Heal Manag (PHM), 2013 IEEE Conf 2013:1–7. doi:10.1109/ICPHM.2013.6621447. [Google Scholar]
  6. Yang ZX, Wang XB, Zhong JH. Representational learning for fault diagnosis of wind turbine equipment: A multi-layered extreme learning machines approach. Energies 2016;9. doi:10.3390/en9060379. [Google Scholar]
  7. Wang L, Zhao X, Pei J, Tang G. Transformer fault diagnosis using continuous sparse autoencoder. Springerplus 2016;5. doi:10.1186/s40064-016-2107-7. [Google Scholar]
  8. Li C, Sánchez RV, Zurita G, Cerrada M, Cabrera D. Fault diagnosis for rotating machinery using vibration measurement deep statistical feature learning. Sensors (Switzerland) 2016;16. doi:10.3390/s16060895. [Google Scholar]
  9. Kane P V, Andhare AB. Application of psychoacoustics for gear fault diagnosis using artificial neural network. J Low Freq Noise, Vib Act Control 2016;35:207–20. doi:10.1177/0263092316660915. [CrossRef] [Google Scholar]
  10. Tran VT, Althobiani F, Ball A. An approach to fault diagnosis of reciprocating compressor valves using Teager-Kaiser energy operator and deep belief networks. Expert Syst Appl 2014;41:4113–22. doi:10.1016/j.eswa.2013.12.026. [CrossRef] [Google Scholar]
  11. Tamilselvan P, Wang P. Failure diagnosis using deep belief learning based health state classification. Reliab Eng Syst Saf 2013;115:124–35. doi:10.1016/j.ress.2013.02.022. [CrossRef] [Google Scholar]
  12. Tamilselvan P, Yibin Wang, Pingfeng Wang. Deep Belief Network based state classification for structural health diagnosis. 2012 IEEE Aerosp. Conf., 2012, p. 1–11. doi:10.1109/AERO.2012.6187366. [Google Scholar]
  13. Li C, Sanchez RV, Zurita G, Cerrada M, Cabrera D, Vásquez RE. Multimodal deep support vector classification with homologous features and its application to gearbox fault diagnosis. Neurocomputing 2015;168:119–27. doi:10.1016/j.neucom.2015.06.008. [CrossRef] [Google Scholar]
  14. Chen Z, Li C, Sánchez R-V. Multi-layer neural network with deep belief network for gearbox fault diagnosis. J Vibroengineering 2015;17:2379–92. [Google Scholar]
  15. Chen Z, Li C, Sanchez R-V. Gearbox Fault Identification and Classification with Convolutional Neural Networks. Shock Vib 2015;2015:1–10. doi:10.1155/2015/390134. [Google Scholar]
  16. Sharma A, Amarnath M, Kankar PK. Feature extraction and fault severity classification in ball bearings. JVC/Journal Vib Control 2016;22:176–92. doi:10.1177/1077546314528021. [CrossRef] [Google Scholar]
  17. Chen Z, Zeng X, Li W, Liao G. Machine fault classification using deep belief network. 2016 IEEE Int. Instrum. Meas. Technol. Conf. Proc., 2016, p. 1–6. doi:10.1109/I2MTC.2016.7520473. [Google Scholar]
  18. Guo X, Chen L, Shen C. Hierarchical adaptive deep convolution neural network and its application to bearing fault diagnosis. Meas J Int Meas Confed 2016;93:490–502. doi:10.1016/j.measurement.2016.07.054. [Google Scholar]
  19. Janssens O, Slavkovikj V, Vervisch B, Stockman K, Loccufier M, Verstockt S, et al. Convolutional Neural Network Based Fault Detection for Rotating Machinery. J Sound Vib 2016;377:331–45. doi:10.1016/j.jsv.2016.05.027. [Google Scholar]
  20. Jia F, Lei Y, Lin J, Zhou X, Lu N. Deep neural networks: A promising tool for fault characteristic mining and intelligent diagnosis of rotating machinery with massive data. Mech Syst Signal Process 2016;72-73:303–15. doi:10.1016/j.ymssp.2015.10.025. [Google Scholar]
  21. Zhao R, Yan R, Wang J, Mao K. Learning to monitor machine health with convolutional Bi- directional LSTM networks. Sensors (Switzerland) 2017;17. doi:10.3390/s17020273. [Google Scholar]
  22. Sun W, Shao S, Zhao R, Yan R, Zhang X, Chen X. A sparse auto-encoder-based deep neural network approach for induction motor faults classification. Meas J Int Meas Confed 2016;89:171–8. doi:10.1016/j.measurement.2016.04.007. [Google Scholar]
  23. Lu C, Wang ZY, Qin WL, Ma J. Fault diagnosis of rotary machinery components using a stacked denoising autoencoder-based health state identification. Signal Processing 2017;130:377–88. doi:10.1016/j.sigpro.2016.07.028. [CrossRef] [Google Scholar]
  24. Jie T, Yi-lun L, Fang T, Chi L. Fault Diagnosis of Rolling Bearing using Deep Belief Networks. Int Symp Mater Energy Environ Eng 2016:566–9. [Google Scholar]
  25. Shao H, Jiang H, Wang F, Wang Y. Rolling bearing fault diagnosis using adaptive deep belief network with dual-tree complex wavelet packet. ISA Trans 2017;69:187–201. doi:10.1016/j.isatra.2017.03.017. [CrossRef] [Google Scholar]
  26. Wen L, Gao L, Li X. A New Deep Transfer Learning Based on Sparse Auto-Encoder for Fault Diagnosis. IEEE Trans Syst Man, Cybern Syst 2017. doi:10.1109/TSMC.2017.2754287. [Google Scholar]
  27. Haidong S, Hongkai J, Xingqiu L, Shuaipeng W. Intelligent fault diagnosis of rolling bearing using deep wavelet auto-encoder with extreme learning machine. Knowledge-Based Syst 2017. doi:10.1016/j.knosys.2017.10.024. [Google Scholar]
  28. Tan J, Lu W, An J, Wan X. Fault diagnosis method study in roller bearing based on wavelet transform and stacked auto-encoder. 27th Chinese Control Decis Conf (2015 CCDC) 2015:4608–13. doi:10.1109/ccdc.2015.7162738. [Google Scholar]
  29. Verstraete D, Ferrada A, Droguett EL, Meruane V, Modarres M, Meruane V, et al. Deep Learning Enabled Fault Diagnosis Using Time- Frequency Image Analysis of Rolling Element Bearings. Shock Vib 2017;2017:1–17. doi:10.1155/2017/5067651. [CrossRef] [Google Scholar]
  30. Xie Y, Zhang T. Feature extraction based on DWT and CNN for rotating machinery fault diagnosis. Proc. 29th Chinese Control Decis. Conf. CCDC 2017, 2017, p. 3861–6. doi:10.1109/CCDC.2017.7979176. [CrossRef] [Google Scholar]
  31. Lee KB, Cheon S, Kim CO. A convolutional neural network for fault classification and diagnosis in semiconductor manufacturing processes. IEEE Trans Semicond Manuf 2017;30:135–42. doi:10.1109/TSM.2017.2676245. [CrossRef] [Google Scholar]
  32. Li S, Liu G, Tang X, Lu J, Hu J. An Ensemble Deep Convolutional Neural Network Model with Improved D-S Evidence Fusion for Bearing Fault Diagnosis. Sensors 2017;17:1729. doi:10.3390/s17081729. [CrossRef] [Google Scholar]
  33. Lu C, Wang Z, Zhou B. Intelligent fault diagnosis of rolling bearing using hierarchical convolutional network based health state classification. Adv Eng Informatics 2017;32:139–51. doi:10.1016/j.aei.2017.02.005. [Google Scholar]
  34. Zhang X, Wang B, Chen X. Intelligent fault diagnosis of roller bearings with multivariable ensemble-based incremental support vector machine. Knowledge-Based Syst 2015;89:56–85. doi:10.1016/j.knosys.2015.06.017. [CrossRef] [Google Scholar]
  35. Muruganatham B, Sanjith MA, Krishnakumar B, Satya Murty SA V. Roller element bearing fault diagnosis using singular spectrum analysis. Mech Syst Signal Process 2013;35:150–66. doi:10.1016/j.ymssp.2012.08.019. [Google Scholar]
  36. Huo Z, Zhang Y, Francq P, Shu L, Huang J. Incipient Fault Diagnosis of Roller Bearing Using Optimized Wavelet Transform Based Multi-Speed Vibration Signatures. IEEE Access 2017;5:19442–56. doi:10.1109/ACCESS.2017.2661967. [CrossRef] [Google Scholar]
  37. Chen F, Tang B, Song T, Li L. Multi-fault diagnosis study on roller bearing based on multi- kernel support vector machine with chaotic particle swarm optimization. Meas J Int Meas Confed 2014;47:576–90. doi:10.1016/j.measurement.2013.08.021. [CrossRef] [Google Scholar]
  38. Kateris D, Moshou D, Pantazi XE, Gravalos I, Sawalhi N, Loutridis S. A machine learning approach for the condition monitoring of rotating machinery. J Mech Sci Technol 2014;28:61–71. doi: DOI 10.1007/s12206-013-1102-y. [CrossRef] [Google Scholar]
  39. Ince T, Kiranyaz S, Eren L, Askar M, Gabbouj M. Real-Time Motor Fault Detection by 1-D Convolutional Neural Networks. IEEE Trans Ind Electron 2016;63:7067–75. doi:10.1109/TIE.2016.2582729. [CrossRef] [Google Scholar]
  40. Serre T, Kouh M, Cadieu C, Knoblich U, Kreiman G, Poggio T. A theory of object recognition: Computations and circuits in the feedforward path of the ventral stream in primate visual cortex. Artif Intell 2005:1–130. doi: [Google Scholar]
  41. Serre T, Oliva A, Poggio T. A feedforward architecture accounts for rapid categorization. Proc Natl Acad Sci 2007;104:6424–9. doi:10.1073/pnas.0700622104. [CrossRef] [Google Scholar]
  42. Lawrence S, Giles CL, Tsoi AC, Back AD. Face recognition: A convolutional neural-network approach. IEEE Trans Neural Networks 1997;8:98–113. doi:10.1109/72.554195. [Google Scholar]
  43. Shea KO, Nash R. An Introduction to Convolutional Neural Networks. ArXiv 2015:1–8. [Google Scholar]
  44. Lu W, Liang B, Cheng Y, Meng D, Yang J, Zhang T. Deep Model Based Domain Adaptation for Fault Diagnosis. IEEE Trans Ind Electron 2017;64:2296–305. doi:10.1109/TIE.2016.2627020. [CrossRef] [Google Scholar]
  45. Le Cun Jackel, B. Boser, J. S. Denker, D. Henderson, R. E. Howard, W. Hubbard LD, Cun B Le, Denker J, Henderson D. Handwritten Digit Recognition with a Back-Propagation Network. Adv Neural Inf Process Syst 1990:396–404. doi:10.1111/dsu.12130. [Google Scholar]
  46. Lee D, Siu V, Cruz R, Yetman C. Convolutional neural net and bearing fault analysis. Proc. Int. Conf. Data Min., The Steering Committee of The World Congress in Computer Science, Computer Engineering and Applied Computing (WorldComp); 2016, p. 194. [Google Scholar]
  47. Zhang W, Peng G, Li C. Bearings Fault Diagnosis Based on Convolutional Neural Networks with 2-D Representation of Vibration Signals as Input. MATEC Web Conf., vol. 95, 2017. doi:10.1051/matecconf/20179513001. [Google Scholar]
  48. Wang J, Zhuang J, Duan L, Cheng W. A multi- scale convolution neural network for featureless fault diagnosis. Int. Symp. Flex. Autom. ISFA 2016, 2016, p. 65–70. doi:10.1109/ISFA.2016.7790137. [Google Scholar]
  49. Bhadane M, Ramachandran KI. Bearing fault identification and classification with convolutional neural network. 2017 Int. Conf. Circuit, Power Comput. Technol., 2017, p. 1–5. doi:10.1109/ICCPCT.2017.8074401. [Google Scholar]
  50. Ren S, He K, Girshick R, Sun J, Donahue J, Darrell T, et al. Rich feature hierarchies for accurate object detection and semantic segmentation. Proc IEEE Comput Soc Conf Comput Vis Pattern Recognit 2015;794:1–15. doi:10.1016/j.nima.2015.05.028. [Google Scholar]
  51. Tompson J, Goroshin R, Jain A, Lecun Y, Bregler C. Efficient Object Localization Using Convolutional Networks. Cvpr 2015:2014. doi:10.1109/CVPR.2015.7298664. [Google Scholar]
  52. Jing L, Zhao M, Li P, Xu X. A convolutional neural network based feature learning and fault diagnosis method for the condition monitoring of gearbox. Measurement 2017;111:1–10. doi:10.1016/j.measurement.2017.07.017. [CrossRef] [Google Scholar]
  53. Zhao W. Research on the deep learning of the small sample data based on transfer learning. AIP Conf Proc 2017;1864:20018. doi:10.1063/1.4992835. [Google Scholar]

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