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Cited by 630

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Failure diagnosis using deep belief learning based health state classification

Tamilselvan, Prasanna, Wang, Pingfeng·2013·Reliability Engineering & System Safety
DOI 10.1016/j.ress.2013.02.022
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Research works printed but not linked: 49

  1. Licht, 2003, Hierarchically organized Bayesian networks for distributed sensor networks, Ameican Society of Mechanical Engineering, Dynamic System Control Division, 71, 1059
  2. Marseguerra, 2002, Condition-based maintenance optimization by means of genetic algorithms and Monte Carlo simulation, Reliability Engineering and System Safety, 77, 151, 10.1016/S0951-8320(02)00043-1 · doi:10.1016/s0951-8320(02)00043-1
  3. Zio, 2009, Review reliability engineering: old problems and new challenges, Reliability Engineering and System Safety, 94, 125, 10.1016/j.ress.2008.06.002 · DOI · Paper page
  4. Dekker, 1996, Applications of maintenance optimization models: a review and analysis, Reliability Engineering and System Safety, 51, 229, 10.1016/0951-8320(95)00076-3 · DOI · Paper page
  5. Barata, 2002, Simulation modelling of repairable multi-component dteriorating systems for ‘on condition’ maintenance optimization, Reliability Engineering and System Safety, 76, 255, 10.1016/S0951-8320(02)00017-0 · doi:10.1016/s0951-8320(02)00017-0
  6. Grall, 2002, A condition-based maintenance policy for stochastically deteriorating systems, Reliability Engineering and System Safety, 76, 167, 10.1016/S0951-8320(01)00148-X · doi:10.1016/s0951-8320(01)00148-x
  7. JAM, 2010, Discounted cost model for condition-based maintenance optimization, Reliability Engineering and System Safety, 95, 236, 10.1016/j.ress.2009.10.004 · doi:10.1016/j.ress.2009.10.004
  8. Smidt-Destombes, 2004, On the availability of a k-out-of-N system given limited spares and repair capacity under a condition based maintenance strategy, Reliability Engineering and System Safety, 83, 287, 10.1016/j.ress.2003.10.004 · doi:10.1016/j.ress.2003.10.004
  9. Coit, 2000, Gamma distribution parameter estimation for field reliability data with missing failure times, IIE Transactions, 32, 1161, 10.1080/07408170008967470 · doi:10.1080/07408170008967470
  10. Elsayed, 2000, Perspectives and challenges for research in quality and reliability engineering, International Journal of Production Research, 38, 1953, 10.1080/002075400188438 · doi:10.1080/002075400188438
  11. Myotyri, 2006, Application of stochastic filtering for lifetime prediction, Reliability Engineering and System Safety, 91, 200, 10.1016/j.ress.2005.01.002 · DOI
  12. Tinga, 2010, Application of physical failure models to enable usage and load based maintenance, Reliability Engineering and System Safety, 95, 1061, 10.1016/j.ress.2010.04.015 · DOI
  13. Cadini, 2009, Model-based Monte Carlo state estimation for condition-based component replacement, Reliability Engineering and System Safety, 94, 752, 10.1016/j.ress.2008.08.003 · DOI
  14. Zio, 2010, A data-driven fuzzy approach for predicting the remaining useful life in dynamic failure scenarios of a nuclear power plant, Reliability Engineering and System Safety, 95, 49, 10.1016/j.ress.2009.08.001 · DOI · Paper page
  15. Baraldi, 2011, Classifier-ensemble incremental-learning procedure for nuclear transient identification at different operational conditions, Reliability Engineering and System Safety, 98, 480, 10.1016/j.ress.2010.11.005 · DOI
  16. Niu, 2010, Development of an optimized condition-based maintenance system by data fusion and reliability-centered maintenance, Reliability Engineering and System Safety, 95, 786, 10.1016/j.ress.2010.02.016 · DOI · Paper page
  17. Alguindigue, 1993, Monitoring and diagnosis of rolling element bearings using artificial neural networks, IEEE Transactions on Industrial Electronics, 40, 209, 10.1109/41.222642 · DOI
  18. Gebraeel, 2002, Vibration-based condition monitoring of thrust bearings for maintenance management, Intelligent Engineering System Through Artificial Neural Network, 12, 543
  19. Li, 1999, Dynamic prognostic prediction of defect propagation on rolling element bearings, Lubrication Engineering, 42, 385
  20. Martin, 1994, Review by discussion of condition monitoring and fault diagnosis in machine tools, International Journal of Machine Tools and Manufacture, 34, 527, 10.1016/0890-6955(94)90083-3 · DOI
  21. Booth, 1998, The use of artificial neural networks for condition monitoring of electrical power transformers, Neurocomputing, 23, 97, 10.1016/S0925-2312(98)00064-2 · doi:10.1016/s0925-2312(98)00064-2
  22. Macian, 2003, Analytical approach to wear rate determination for internal combustion engine condition monitoring based on oil analysis, Tribology International, 36, 771, 10.1016/S0301-679X(03)00060-4 · doi:10.1016/s0301-679x(03)00060-4
  23. Breikin T, Kulikov G, Arkov V, Fleming P. Dynamic modelling for condition monitoring of gas turbines: genetic algorithms approach. In: Proceedings of the 16th IFAC World Congress; 2005. · doi:10.3182/20050703-6-cz-1902.00124
  24. Arel, 2010, Deep machine learning—a new frontier in artificial intelligence research, Computational Intelligence Magazine, 5, 13, 10.1109/MCI.2010.938364 · DOI · Paper page
  25. Huang, 2007, Residual life predictions for ball bearings based on self-organizing map and back propagation neural network methods, Mechanical Systems and Signal Processing, 21, 193, 10.1016/j.ymssp.2005.11.008 · DOI
  26. Khomfoi, 2007, Fault diagnostic system for a multilevel inverter using a neural network, IEEE Transactions on Power Electronics, 22, 1062, 10.1109/TPEL.2007.897128 · DOI · Paper page
  27. Samanta, 2004, Gear fault detection using artificial neural networks and support vector machines with genetic algorithms, Mechanical Systems and Signal Processing, 18, 625, 10.1016/S0888-3270(03)00020-7 · DOI · Paper page
  28. Yang, 2005, Condition classification of small reciprocating compressor for refrigerators using artificial neural networks and support vector machines, Mechanical Systems and Signal Processing, 19, 371, 10.1016/j.ymssp.2004.06.002 · DOI
  29. Wong, 2006, Modified self-organising map for automated novelty detection applied to vibration signal monitoring, Mechanical Systems and Signal Processing, 20, 593, 10.1016/j.ymssp.2005.01.008 · DOI
  30. Abbasion, 2007, Rolling element bearings multi-fault classification based on the wavelet denoising and support vector machine, Mechanical Systems and Signal Processing, 21, 2933, 10.1016/j.ymssp.2007.02.003 · DOI · Paper page
  31. Wang P, Youn BD, Hu CA. Probabilistic detectability-based structural sensor network design methodology for prognostics and health management. In: Annual conference of the prognostics and health management society; 2010. · doi:10.36001/phmconf.2010.v2i1.1805
  32. Cheng, 2012, Anomaly detection of polymer resettable circuit protection devices, IEEE Transactions on Device and Materials Reliability, 12, 420, 10.1109/TDMR.2011.2170689 · DOI
  33. Cheng, 2012, Using cross-validation for model parameter selection of sequential probability ratio test, Expert Systems with Applications, 39, 8467, 10.1016/j.eswa.2012.01.172 · doi:10.1016/j.eswa.2012.01.172
  34. Zhang, 2010, Bearing fault diagnosis using multi-scale entropy and adaptive neuro-fuzzy inference, Expert Systems with Applications, 37, 6077, 10.1016/j.eswa.2010.02.118 · DOI · Paper page
  35. Saimurugan, 2011, Multi component fault diagnosis of rotational mechanical system based on decision tree and support vector machine, Expert Systems with Applications, 38, 3819, 10.1016/j.eswa.2010.09.042 · DOI
  36. Geramifard O, Xu JX, Pang C, Zhou J, Li X. Data-driven approaches in health condition monitoring—a comparative study. In: Proceedings of the 8th IEEE international conference on control and automation (ICCA); 2010. p. 1618–22. · doi:10.1109/icca.2010.5524339
  37. Hinton, 2010, A practical guide to training restricted Boltzmann machines, Momentum, 9, 1
  38. Lee H, Grosse R, Ranganath R, Ng AY. Convolutional deep belief networks for scalable unsupervised learning of hierarchical representations. In: International conference on machine learning. Montreal, Canada; 2009. p. 609–16. · DOI · Paper page
  39. Fisher, 1936, The use of multiple measurements in taxonomic problems, Annual Eugenics, 7, 179, 10.1111/j.1469-1809.1936.tb02137.x · DOI · Paper page
  40. Mangasarian, 1995, Breast cancer diagnosis and prognosis via linear programming, Operations Research, 43, 570, 10.1287/opre.43.4.570 · DOI · Paper page
  41. Nakai, 1992, Knowledge base for predicting protein localization sites in eukaryotic cells, Genomics, 14, 897, 10.1016/S0888-7543(05)80111-9 · DOI · Paper page
  42. Hu C, Youn BD, Wang P. Ensemble of data-driven prognostic algorithms with weight optimization and K-fold cross validation. In: Annual conference of the prognostics and health management society; 2010. · doi:10.1115/detc2010-29182
  43. Wang T, Yu J, Diegel S, Lee J. A similarity-based prognostics approach for remaining useful life estimation of engineered systems. In: Proceedings of the international conference on prognostics and health management; 2008. · DOI · Paper page
  44. Gao P, Woo W, Dlay S. Neural network approaches to nonlinear blind source separation. In: Proceedings of the 8th international symposium on signal processing and its applications; 2005. · doi:10.1109/isspa.2005.1580200
  45. Leibfield, 1998, Online monitors keep transformers in service, IEEE Computer Applications in Power, 11, 36, 10.1109/67.694934 · doi:10.1109/67.694934
  46. Rivera, 2000, Measurements of mechanical vibrations at magnetic cores of power transformers with fiber-optic interferometric intrinsic sensor, IEEE Journal on Selected Topics in Quantum Electronics, 6, 788, 10.1109/2944.892619 · doi:10.1109/2944.892619
  47. Allan, 1992, New techniques for monitoring the insulation quality of in-service HV apparatus, IEEE Transactions on Electrical Insulation, 27, 578, 10.1109/14.142722 · doi:10.1109/14.142722
  48. Bengio Y, Delalleau O, Le Roux N. The curse of highly variable functions for local kernel machines. In: Proceedings of the advances in neural information processing systems (NIPS'05), p. 18.
  49. Larochelle, 2009, Exploring strategies for training deep neural networks, Journal of Machine Learning Research, 10, 1

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Metadata

Authors
Tamilselvan, P., & Wang, P.
Venue
Reliability Engineering & System Safety
Publisher
Elsevier BV
Published
2013-07
Volume
115
Pages
124-135
ISSN
0951-8320 (ISSN-L)
DOI
10.1016/j.ress.2013.02.022
OpenAlex
W2063922127
Cited by
630
References
5
FWCI
25.87
Type
journal article
Language
en
Open access
closed
Licence
www.elsevier.com/tdm/userlicense/1.0/ (tdm)
Funders
  • National Science Foundation (CMMI#1200597)
  • Wichita State University through the University Research Creative Project Awards (UCRA)

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CoChat. (2026). Citation check: Failure diagnosis using deep belief learning based health state classification. https://app.cochat.ai/papers/10.1016/j.ress.2013.02.022
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Tamilselvan, P., & Wang, P. (2013). Failure diagnosis using deep belief learning based health state classification. Reliability Engineering & System Safety. https://doi.org/10.1016/j.ress.2013.02.022

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