EDP Sciences logo
Submit your paper
Search
Menu
All issues Volume 26 (2025) Mechanics & Industry, 26 (2025) 17 References
Open Access
Issue
Mechanics & Industry
Volume 26, 2025
Article Number 17
Number of page(s) 18
DOI https://doi.org/10.1051/meca/2025007
Published online 24 April 2025
  1. Q.-C. Peng, Research on topology optimization of wind power gear system with multi-source excitation and dynamic response [D]. Chongqing University, 2022. [Google Scholar]
  2. E.J. Alvarez, A.P. Ribaric, An improved-accuracy method for fatigue load analysis of wind turbine gearbox based on SCADA, Renew. Energy 115, 391-399 (2018) [Google Scholar]
  3. L. Zhang, N. Hu, Fault diagnosis of sun gear based on continuous vibration separation and minimum entropy deconvolution, Measurement 141, 332-344 (2019) [CrossRef] [Google Scholar]
  4. J. Ribrant, L. Bertling, Survey of failures in wind power systems with focus on Swedish wind power plants during 1997-2005, in 2007 IEEE power engineering society general meeting. IEEE (2007), pp. 1-8 [Google Scholar]
  5. C. Dao, B. Kazemtabrizi, C. Crabtree, Wind turbine reliability data review and impacts on levelised cost of energy, Wind Energy 22, 1848-1871 (2019) [CrossRef] [Google Scholar]
  6. P. Veers, Wind energy modeling and simulation. Volume 2: Turbine and system (The Institution of Engineering and Technology, London, 2019) [Google Scholar]
  7. J. Carroll, A. McDonald, D. McMillan, Failure rate, repair time and unscheduled O & M cost analysis of offshore wind turbines, Wind Energy 19, 1107-1119 (2016) [CrossRef] [Google Scholar]
  8. Gearbox Reliability Database[EB/OL]. https://grd.nrel.gov/stats. [Google Scholar]
  9. D. Busse, J. Erdman, R.J. Kerkman Bearing currents and their relationship to PWM drives, IEEE Trans. Power Electr. 12, 243-252 (1997) [CrossRef] [Google Scholar]
  10. S. Faulstich, B. Hahn, P.J. Tavner, Wind turbine downtime and its importance for offshore deployment, Wind Energy 14, 327-337 (2011) [CrossRef] [Google Scholar]
  11. W. Qiao, D. Lu, A survey on wind turbine condition monitoring and fault diagnosis—Part I: Components and subsystems, IEEE Trans. Ind. Electr. 62, 6536-6545 (2015) [CrossRef] [Google Scholar]
  12. Z. Hameed, Y.S. Hong, Y.M. Cho, Condition monitoring and fault detection of wind turbines and related algorithms: a review, Renew. Sustain. Energy Rev. 13, 1-39 (2009) [CrossRef] [Google Scholar]
  13. Y. Amirat, M.E.H. Benbouzid, E. Al-Ahmar, A brief status on condition monitoring and fault diagnosis in wind energy conversion systems, Renew. Sustain. Energy Rev. 13, 2629-2636 (2009) [CrossRef] [Google Scholar]
  14. W. Yang, P.J. Tavner, C.J. Crabtree, Wind turbine condition monitoring: technical and commercial challenges, Wind Energy 17, 673-693 (2014) [CrossRef] [Google Scholar]
  15. H. Fazlollahtabar, S.T.A. Niaki, Fault tree analysis for reliability evaluation of an advanced complex manufacturing system, J. Adv. Manufactur. Syst. 17, 107-118 (2018) [CrossRef] [Google Scholar]
  16. M. Darwish, S. Almouahed, F. de Lamotte, The integration of expert-defined importance factors to enrich Bayesian Fault Tree Analysis, Reliab. Eng. Syst. Saf. 162, 81-90 (2017) [CrossRef] [Google Scholar]
  17. U. Bhardwaj, A.P. Teixeira, C.G. Soares, Reliability prediction of an offshore wind turbine gearbox, Renew. Energy 141, 693-706 (2019) [Google Scholar]
  18. L. Cui, Y. Zhang, F. Zhang et al., Vibration response mechanism of faulty outer race rolling element bearings for quantitative analysis, J. Sound Vibr. 364, 67-76 (2016) [CrossRef] [Google Scholar]
  19. H. Yang, W. Shi, L. Guo et al., Study on mesh stiffness and sensitivity analysis of planetary gear system considering the deformation effect of carrier and bearing, Eng. Failure Anal. 135, 106146 (2022) [CrossRef] [Google Scholar]
  20. Z. Ma, M. Zhao, B. Li et al., A novel blind deconvolution based on sparse subspace recoding for condition monitoring of wind turbine gearbox, Renew. Energy 170, 141-162 (2021) [Google Scholar]
  21. X. Cao, B. Chen, N. Zeng, A deep domain adaption model with multi-task networks for planetary gearbox fault diagnosis, Neurocomputing 409, 173-190 (2020) [CrossRef] [Google Scholar]
  22. A. Heydari, D.A. Garcia, A. Fekih et al., A hybrid intelligent model for the condition monitoring and diagnostics of wind turbines gearbox, IEEE Access 9, 89878-89890 (2021) [CrossRef] [Google Scholar]
  23. P. Sun, J. Li, C. Wang et al., A generalized model for wind turbine anomaly identification based on SCADA data, Appl. Energy 168, 550-567 (2016) [CrossRef] [Google Scholar]
  24. P.R. Sonawane, M. Chandrasekaran, Investigation of gear pitting defect using vibration characteristics in a single-stage gearbox, Int. J. Electr. Eng. Educ. 57, 272-278 (2020) [CrossRef] [Google Scholar]
  25. L. Fei, L. Yinglei, M. Xueming et al., Fault tree analysis using Bayesian optimization: a reliable and effective fault diagnosis approaches, J. Failure Anal. Prevent. 21, 619-630 (2021) [CrossRef] [Google Scholar]
  26. S. Shoar, A. Banaitis, Application of fuzzy fault tree analysis to identify factors influencing construction labor productivity: a high-rise building case study, J. Civil Eng. Manag. 25, 41-52 (2019) [CrossRef] [Google Scholar]
  27. L. Guan, Q. Liu, A. Abbasi et al., Develo a comprehensive risk assessment model based on fuzzy Bayesian belief network (FBBN), J. Civil Eng. Manag. 26, 614-634 (2020) [CrossRef] [Google Scholar]
  28. S. Yang, J. Yang, Y. Cui et al., Study on the EMI impact over the safety of railway signaling and case analysis, in 2017 2nd international conference on system reliability and safety (ICSRS). IEEE (2017), pp. 374-379. [Google Scholar]
  29. H. Tanaka, L.T. Fan, F.S. Lai et al., Fault-tree analysis by fuzzy probability, IEEE Trans. Reliab. 32, 453-457 (1983) [CrossRef] [Google Scholar]
  30. C.T. Lin, M.J.J. Wang, Hybrid fault tree analysis using fuzzy sets, Reliab. Eng. Syst. Saf. 58, 205-213 (1997) [CrossRef] [Google Scholar]
  31. F. Li, Z. Yuan, Z. He et al., Reliability analysis of hydraulic circuit of truck crane outrigger based on T-S fuzzy fault tree model, Mach. Tools Hydraul. 46, 160-163 (2018) [Google Scholar]
  32. L. Cuiyu, S. Xinmin, J. Xu, C. Fangjie, Z. Guanghua, Reliability analysis of domestic air conditioners based on T-S fuzzy fault tree, Mech. Des. 38, 120-126 (2021) [Google Scholar]
  33. H. Song, H.-A. Zhang, X. Wang, T-S fuzzy fault tree analysis method, Control Decis. Mak. 854-859 (2005) [Google Scholar]
  34. R. Zhang, J. Liu, S. Fang, Z. Gu, Research on ship shaft system troubleshooting method based on T-S fuzzy fault tree, China Ship Repair 35, 54-58 (2022) [Google Scholar]
  35. L. Huang, L. Lan, J. Qi, Q. Zhang, Fault diagnosis analysis of sliding beacon based on T-S fuzzy fault tree, Sci. Technol. Eng. 23, 6661-6666 (2023) [Google Scholar]
  36. S. Quan, Research on fault tree analysis method of CNC tool holder considering uncertainty and fault polymorphism. Jilin University (2023) [Google Scholar]
  37. Z. Bi, C. Li, X. Li et al., Research on fault diagnosis for pumping station based on TS fuzzy fault tree and Bayesian network, J. Electr. Comput. Eng. 2017 (2017) [Google Scholar]
  38. D. Lijing, S. Hua, L. Wenjing et al., The application of TS fuzzy fault tree analysis in satellite attitude control system, in IEEE 10th International Conference on Industrial Informatics. IEEE (2012), 208-213. [Google Scholar]
  39. G. Li-Long, L. Jiu-Lin, Y. Yu, H. He, Fault diagnosis of hydraulic system based on T-S fuzzy fault tree, Hydraulic Pneumat. Seals 41, 76-80 (2021) [Google Scholar]
  40. J. Xin, G. Guang, B. Fan, Research on system fault diagnosis based on T-S fuzzy fault tree, Electr. Design Eng. 21, 156-159 (2013) [Google Scholar]
  41. C. Yao, Y. Zhang, D. Chen, X. Wang, Research on T-S fuzzy importance analysis method, J. Mech. Eng. 47, 163-169 (2011) [CrossRef] [Google Scholar]
  42. C.-Y. Yao, Y.-Y. Zhang, X.-F. Wang, D.-N. Chen, T-S fuzzy fault tree importance analysis method, China Mech. Eng. 22, 1261-1268 (2011) [Google Scholar]
  43. C. Hongtuan, Z. Aijia, L. Tengjiao et al., Fault tree-based inference fault diagnosis of complex equipment with fuzzy Bayesian network, Syst. Eng. Electr. Technol. 43, 1248-1261 (2021) [Google Scholar]
  44. H.L. Zhu, S.S. Liu, Y.Y. Qu et al., A new risk assessment method based on belief rule base and fault tree analysis, Proc. Inst. Mech. Eng. O 236, 420-438 (2022) [CrossRef] [Google Scholar]
  45. L. Wan, L. Niu, C. Hu, FTA-BN based fault diagnosis strategy for hybrid vehicles, J. Anhui Univ. Technol. (Natural Science Edition) 40, 158-165 (2023) [Google Scholar]
  46. C. Zhang, Z. Zhang, X. Chen et al., Risk assessment of industrial production safety accidents based on Bayesian network model, Gas Heat 43, 29-36 (2023) [Google Scholar]
  47. P. Antal et al., Annotated Bayesian Networks: a tool to integrate textual and probabilistic medical knowledge, in Proceedings 14th IEEE Symposium on Computer-Based Medical Systems. CBMS 2001. IEEE (2001) [Google Scholar]
  48. P.P. Shenoy, J.C. West, Inference in hybrid Bayesian networks using mixtures of polynomials, Int. J. Approx. Reas. 52, 641-657 (2011) [CrossRef] [Google Scholar]
  49. S.-R. Zheng, W.-J. Pan, Y.-J. Jiang et al., Mechanism analysis of wake accident based on fuzzy set theory-Bayesian network, Ship Electr. Eng. 42, 135-141 (2022) [Google Scholar]
  50. F. Liang, Z. Wang. Reliability analysis of welding machine based on T-S fuzzy fault tree, Mech. Strength 39, 592-597 (2017) [Google Scholar]
  51. Y. Yang, Reliability analysis of high pressure piston pump based on T-S fuzzy fault tree, Petrol. Mining Mach. 49, 27-31 (2020) [Google Scholar]
  52. B. Bai, J. Zhang, C. Zhou, C. Zhang, Reliability analysis of industrial robots based on T-S fuzzy fault tree, Mech. Strength 43, 1348-1358 (2021) [Google Scholar]
  53. B.Y. Li, Q.G. Liu, Decision-making research on safety risk factors of high-speed rail station by integrating fuzzy fault tree and Bayesian network, Railway Standard Des. 1-10 (2024). https://doi.org/10.13238/j.issn.1004-2954.202212190002. [Google Scholar]
  54. S. Yang, K. Xu, Study on the leakage possibility of overhead gas pipeline in plant area based on FUZZY-BN-FTA, China Saf. Product. Sci. Technol. 20, 82-89 (2024) [Google Scholar]
  55. T. Yuan, Research on safety risk assessment of dangerous goods highway transportation based on BN-bow-tie model. Southwest Jiaotong University (2024) [Google Scholar]
  56. M.M. Aliabadi, A. Pourhasan, I. Mohammadfam, Risk modelling of a hydrogen gasholder using Fuzzy Bayesian Network (FBN), Int. J. Hydrogen Energy 45, 1177-1186 (2020) [CrossRef] [Google Scholar]
  57. F. Yan, Y. Jianxing, Risk analysis of semi-submersible platform installation based on multistate fuzzy Bayesian network, Ocean Eng. 41, 12-21 (2023) [Google Scholar]
  58. W. Wang, X. He, Y. Li et al., Risk analysis on corrosion of submarine oil and gas pipelines based on hybrid Bayesian network, Ocean Eng. 260, 111957 (2022) [CrossRef] [Google Scholar]
  59. G.C. Avontuur, K. van der Werff, Systems reliability analysis of mechanical and hydraulic drive systems, Reliab. Eng. Syst. Saf. 77, 121-130 (2002) [CrossRef] [Google Scholar]
  60. Y. Chengyu, Z. Jingyi, Reliability-based design and analysis on hydraulic system for synthetic rubber press, Chin. J. Mech. Eng. English Edn. 18, 159 (2005) [CrossRef] [Google Scholar]
  61. Y. Li, F.P.A. Coolen, Time-dependent reliability analysis of wind turbines considering load-sharing using fault tree analysis and Markov chains, Proc. Inst. Mech. Eng. O 233, 1074-1085 (2019) [Google Scholar]
  62. L. Chen, Reliability analysis of wind power gearbox based on fault tree and Monte Carlo simulation, J. Phys.: Conf. Ser. 2584, 012102 (2023) [CrossRef] [Google Scholar]
  63. A. He, Q. Zeng, Y. Zhang et al., A fault diagnosis analysis of afterburner failure of aeroengine based on fault tree, Processes 11, 2086 (2023) [CrossRef] [Google Scholar]
  64. P.R. Sonawane, S. Bhandari, R.B. Patil et al., Reliability and criticality analysis of a large-scale solar photovoltaic system using fault tree analysis approach, Sustainability 15, 4609 (2023) [CrossRef] [Google Scholar]
  65. H. Chen, J. Zhang, C. Li et al., Reliability assignment of a heavy-duty CNC machine tool spindle system based on fault tree analysis, Int. J. Reliab. Saf. 16, 87-109 (2022) [CrossRef] [Google Scholar]
  66. W. Li, G. Liu, Dynamic reliability analysis approach based on fault tree and new process capability index, Qual. Reliab. Eng. Int. 38, 800-816 (2022) [CrossRef] [Google Scholar]
  67. R. Yang, Y. Deng, Analysis on security risks in tunnel construction based on the fault tree analysis, IOP Conf. Ser.: Earth Environ. Sci. 638, 012089 (2021) [CrossRef] [Google Scholar]

Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.

Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.

Initial download of the metrics may take a while.