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All issues Volume 22 (2021) Mechanics & Industry, 22 (2021) 24 References
Open Access
Issue
Mechanics & Industry
Volume 22, 2021
Article Number 24
Number of page(s) 10
DOI https://doi.org/10.1051/meca/2021022
Published online 09 April 2021
  1. H. Zhang, X. Li, L. Jiang, D. Yang, A review of misalignment of aero-engine rotor system, Acta Aeronautica et Astronautica Sinica 40 , 42–53 (2019) [Google Scholar]
  2. Q. Han, M. Wang, G. Zhao, G. Feng, A review of rotor systems with misalignment, Journal of Dynamics and Control 14 , 1–13 (2016) [Google Scholar]
  3. M. Lal, R. Tiwari, Experimental estimation of misalignment effects in rotor-bearing-coupling systems, in: P. Pennacchi (Ed.), Proceedings of the 9th IFToMM International Conference on Rotor Dynamics, Springer, Cham 2015 [Google Scholar]
  4. J. Rybczynski, The possibility of evaluating turbo-set bearing misalignment defects on the basis of bearing trajectory features, Mechanical Systems and Signal Processing 25 , 521–536 (2011) [Google Scholar]
  5. P. Pennacchi, A.J.S. Vania, Vibration, Diagnosis and model based identification of a coupling misalignment, Sock and Vibration 12 , 293–308 (2005) [Google Scholar]
  6. A.W. Lees, J.K. Sinha, M.I.J.M.S. Friswell, S. Processing, Model-based identification of rotating machines, Mechanical Systems & Signal Processing 23 , 1884–1893 (2009) [Google Scholar]
  7. N.H. Chandra, A.S. Sekhar, Fault detection in rotor bearing systems using time frequency techniques, Mechanical Systems and Signal Processing 72–73 , 105–133 (2016) [Google Scholar]
  8. Y. Xia, J. Pang, L. Yang, Q. Zhao, X.J.A.A. Yang, Study on vibration response and orbits of misaligned rigid rotors connected by hexangular flexible coupling, Applied Acoustics 155 , 286–296 (2019) [Google Scholar]
  9. H. Tejas, K. Patel, D.J.M. Ashish, Systems, S. Processing, Mechanical Systems & Signal Processing 23, 2236–2252 (2009) [Google Scholar]
  10. T.H. Patel, A.K. Darpe, Vibration response of misaligned rotors, Journal of Sound and Vibration 325, 609–628 (2009) [Google Scholar]
  11. K. Schuhmann, K. Kirch, A. Knecht, M. Marszalek, F. Nez, J. Nuber, R. Pohl, I. Schulthess, L. Sinkunaite, M. Zeyen, A. Antognini, Passive alignment stability and auto-alignment of multipass amplifiers based on Fourier transforms, Applied Optics 58 , 2904–2912 (2019) [PubMed] [Google Scholar]
  12. G. Szymanski, L. Wawrzyniuk, Effect of nonconstant mirror misalignment of moving mirror in Fourier transform spectrometer, Optical Engineering 54 , 1–6 (2015) [Google Scholar]
  13. R. Yan, R.X. Gao, X. Chen, Wavelets for fault diagnosis of rotary machines: a review with applications, Signal Processing 96 , 1–15 (2014) [Google Scholar]
  14. Y. Xiao, Y. Hong, X. Chen, W. Chen, The application of Dual-Tree Complex Wavelet Transform (DTCWT) energy entropy in misalignment fault Diagnosis of Doubly-Fed Wind Turbine (DFWT), Entropy 19 , 1–14 (2017) [Google Scholar]
  15. A. Umbrajkaar, A. Krishnamoorthy, Vibration analysis using wavelet transform and fuzzy logic for shaft misalignment, Journal of Vibroengineering 20 , 2855–2865 (2018) [Google Scholar]
  16. L. Yongjian, W. Hongjun, A revised Hilbert-Huang transform and its application to fault diagnosis in a rotor system, Sensors 18, 1–27 (2018) [Google Scholar]
  17. G. Dinardo, L. Fabbiano, G. Vacca, Energy-based indexes for analysis of vibrations from rotating machinery based on the Hilbert-Huang transform, in: E.P. Tomasini (Ed.), Proceedings of the 12th International AIVELA Conference on Vibration Measurements by Laser and Noncontact Techniques: Advances and Applications. (AIP Conference Proceedings) 2016 [Google Scholar]
  18. X. Fu, H. Xu, Y. Lin, Z. Xiao, Vibration analysis on the mechanisms for hydropower unit rotors based on empirical mode decomposition, Journal of Optoelectronics and Advanced Materials 16 , 689–696 (2014) [Google Scholar]
  19. S. Singh, N. Kumar, Combined rotor fault diagnosis in rotating machinery using empirical mode decomposition, Journal of Mechanical Science and Technology 28 , 4869–4876 (2014) [Google Scholar]
  20. Z.R. Hou, Rolling bearing fault diagnosis based on wavelet packet and improved BP neural network for wind turbines, Applied Mechanics and Materials 347–350 , 117–120 (2013) [Google Scholar]
  21. Y. Xue, Z. Li, B. Wang, Z. Zhao, F.J.A.I. Li, Nonlinear feature selection using Gaussian kernel SVM-RFE for fault diagnosis, Applied Intelligence 48 , 3306–3331 (2018) [Google Scholar]
  22. N. Mayadevi, V.P. Mini, R.H. Kumar, S. Prins, Fuzzy-Based Intelligent Algorithm for Diagnosis of Drive Faults in Induction Motor Drive System, Arabian Journal for Science and Engineering 45, 1385–1395 (2020) [Google Scholar]
  23. Y. Dalian, M. Jingjing, Z. Fanyu, T. Jie, W. Guangbin, S. Yiping, Bearing fault diagnosis using a support vector machine optimized by an improved ant lion optimizer, Shock and Vibration 2019, 1–20 (2019) [Google Scholar]
  24. Y. Dalian, L. Yilun, L. Songbai, L. Xuejun, M. Liyong, Gear fault diagnosis based on support vector machine optimized by artificial bee colony algorithm, Mechanism and Machine Theory 90, 219–229 (2015) [Google Scholar]
  25. G.E. Hinton, R.R. Salakhutdinov, Reducing the dimensionality of data with neural networks, Science 313 , 504–507 (2006) [NASA ADS] [CrossRef] [MathSciNet] [PubMed] [Google Scholar]
  26. Z. Ling, X. Li, W. Zou, M. Liu, Joint haze-relevant features selection and transmission estimation via deep belief network for efficient single image dehazing, in: 2018 24th International Conference on Pattern Recognition (ICPR), IEEE, Beijing, China, 2018 [Google Scholar]
  27. Z. Fengyu, Y. Jianqin, Y. Yang, Z. Haiting, Y. Xianfeng, Online recognition of human actions based on temporal deep belief neural network, Acta Automatica Snica 42, 1030–1039 (2016) [Google Scholar]
  28. M. Chen, J. Pan, Q. Zhao, Y. Yan, Multi-task learning in deep neural networks for Mandarin-English code-mixing speech recognition, IEICE Transactions on Information and Systems E99.D , 2554–2557 (2016) [Google Scholar]
  29. Z. Xiaoli, J. Minping, A new Local-Global Deep Neural Network and its application in rotating machinery fault diagnosis, Neurocomputing 366, 215–233 (2019) [Google Scholar]
  30. K. Zhao, H. Shao, Intelligent fault diagnosis of rolling bearing using adaptive deep gated recurrent unit, Neural Processing Letters 51 , 1165–1184 (2020) [Google Scholar]
  31. X. He, J. Ma, Weak fault diagnosis of rolling bearing based on FRFT and DBN, Systems Science & Control Engineering 8 , 57–66 (2020) [Google Scholar]
  32. B. Han, X. Yang, Y. Ren, W. Lan, Comparisons of different deep learning-based methods on fault diagnosis for geared system, International Journal of Distributed Sensor Networks 15 , 1–16 (2019) [Google Scholar]
  33. J. Li, X. Li, D. He, Y. Qu, Unsupervised rotating machinery fault diagnosis method based on integrated SAE-DBN and a binary processor, Journal of Intelligent Manufacturing 31, 1899–1916 (2020) [Google Scholar]
  34. P. Tamilselvan, P. Wang, Failure diagnosis using deep belief learning based health state classification, Reliability Engineering System Safety 115 , 124–135 (2013) [Google Scholar]
  35. X. Jiang, Y. He, G. Li, Y. Liu, X.-P. Zhang, Building damage detection via superpixel-based belief fusion of space-borne SAR and optical images, IEEE Sensors Journal 20 , 2008–2022 (2020) [Google Scholar]
  36. T. Tang, T. Hu, M. Chen, R. Lin, G. Chen, A deep convolutional neural network approach with information fusion for bearing fault diagnosis under different working conditions, Proceedings of the Institution of Mechanical Engineers Part C-Journal of Mechanical Engineering Science 203, 1989–1996 (2020) [Google Scholar]
  37. M.K. Abd Ghani, M.A. Mohammed, N. Arunkumar, S.A. Mostafa, D.A. Ibrahim, M.K. Abdullah, M.M. Jaber, E. Abdulhay, G. Ramirez-Gonzalez, M.A. Burhanuddin, Decision-level fusion scheme for nasopharyngeal carcinoma identification using machine learning techniques, Neural Computing & Applications 32 , 625–638 (2020) [Google Scholar]
  38. L.-x. Luo, Information fusion for wireless sensor network based on mass deep auto-encoder learning and adaptive weighted D-S evidence synthesis, Journal of Ambient Intelligence and Humanized Computing 11 , 519–526 (2020) [Google Scholar]
  39. F. Xiao, A new divergence measure for belief functions in D-S evidence theory for multisensor data fusion, Information Sciences 514 , 462–483 (2020) [Google Scholar]
  40. A. Stief, J.R. Ottewill, J. Baranowski, M. Orkisz, A PCA and two-stage bayesian sensor fusion approach for diagnosing electrical and mechanical faults in induction motors, IEEE Transactions on Industrial Electronics 66 , 9510–9520 (2019) [Google Scholar]
  41. B. Tokhmechi, S. Ebrahimi, H. Azizi, S.R. Ghavami-Riabi, N. Farrokhi, Bayesian data fusion: a reliable approach for descriptive modeling of ore deposits, Journal of Mining and Environment 11 , 63–76 (2020) [Google Scholar]
  42. D. Teekaraman, S. Sendhilkumar, G.S. Mahalakshmi, Semantic provenance based trustworthy users classification on book-based social network using fuzzy decision tree, International Journal of Uncertainty Fuzziness and Knowledge-Based Systems 28 , 47–77 (2020) [Google Scholar]
  43. P. Bhattacharya, On the Dempster-Shafer evidence theory and non-hierarchical aggregation of belief structures, IEEE Transactions on Systems Man and Cybernetics Part A-Systems and Humans 30 , 0–536 (2000) [Google Scholar]
  44. L.A. Zadeh, A simple view of the Dempster-Shafer theory of evidence and its implication for the rule of combination, AI Magazine 7 , 85–90 (1986) [Google Scholar]
  45. A.L. Jousselme, D. Grenier, É. Bossé, A new distance between two bodies of evidence, Information Fusion 2 , 91–101 (2001) [Google Scholar]
  46. L. Ma, F. Zhang, J. Chen, Synthetic rule of evidence based on pignistic probability distance, Computer Engineering and Applications 51 , 61–66 (2015) [Google Scholar]
  47. J. Gonzalez-Lopez, S. Ventura, A. Cano, Distributed multi-label feature selection using individual mutual information measures, Knowledge-Based Systems 188 , 1–10 (2020) [Google Scholar]
  48. L. Zheng, Using mutual information as a cocitation similarity measure, Scientometrics 119 , 1695–1713 (2019) [Google Scholar]
  49. J. Tao, Y. Liu, D. Yang, Bearing fault diagnosis based on deep belief network and multisensor information fusion, Shock and Vibration 2016 (2016) [Google Scholar]

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