Abstract
Heavy-duty gas turbines are usually devised in power plants to generate electrical energy. Sudden failure in any of its parts or subdivisions will result in a decrement of the efficiency of the system or emergency shutdown of the system. The highest risk of failure in these turbines is subjected to the hot gas path (HGP) of the turbine. Due to the existence of uncertainty in diagnosing process or damage growth, in this research, a modified risk-based probabilistic failure analysis model using Bayesian networks (BN) was developed. First, a failure model was developed using the Fault Tree Analysis, and then it is transformed into a BN model. This model is capable of predicting and diagnosing critical components and critical failure modes and mechanisms for each component by updating failure probabilities. Moreover, in order to enhance the application of the proposed model and to identify the risk factors, the sensitivity analysis of the HGP components is presented with applying definitions of importance measures and extend them to BN. The sensitivity analysis and application of its results for making decisions during the system operation will enhance the reliability and safety of the system.
Similar content being viewed by others
References
Abad EMK, Farrahi GH, Abad MMK, Zare AA, Parsa S (2013) Failure analysis of a gas turbine compressor in a thermal power plant. J Fail Anal Prev. https://doi.org/10.1007/s11668-013-9663-8
Aslansefat K (2014) A novel approach for reliability and safety evaluation of control systems with dynamic fault tree. MSc Thesis, Abbaspur Campus, Shahid Beheshti University
Bhandari J, Abbassi R, Garaniya V, Khan F (2015) Risk analysis of deepwater drilling operations using Bayesian network. J Loss Prev Process Ind. https://doi.org/10.1016/j.jlp.2015.08.004
Bobbio A, Portinale L, Minichino M, Ciancamerla E (2001) Improving the analysis of dependable systems by mapping fault trees into Bayesian networks. Reliab Eng Syst Saf. https://doi.org/10.1016/S0951-8320(00)00077-6
Borgonovo E, Apostolakis GE (2001) A new importance measure for risk-informed decision making. Reliab Eng Syst Saf. https://doi.org/10.1016/S0951-8320(00)00108-3
Boudali H, Duga JB (2005) A new Bayesian network approach to solve dynamic fault trees. In: Proceedings annual IEEE, reliability and maintainability Symposium, 2005, pp 451–456
Boyce MP (2011) Gas turbine engineering handbook. Elsevier, New York. https://doi.org/10.1016/B978-0-12-387000-1.01001-9
Cai B, Liu Y, Fan Q, Zhang Y, Liu Z, Yu S, Ji R (2014) Multi-source information fusion based fault diagnosis of ground-source heat pump using Bayesian network. Appl Energy 114:1–9. https://doi.org/10.1016/j.apenergy.2013.09.043
Cai B, Liu H, Xie M (2016) A real-time fault diagnosis methodology of complex systems using object-oriented Bayesian networks. Mech Syst Signal Process. https://doi.org/10.1016/j.ymssp.2016.04.019
Cai B, Kong X, Liu Y, Lin J, Yuan X, Xu H, Ji R (2018) Application of Bayesian networks in reliability evaluation. IEEE Trans Ind Inform. https://doi.org/10.1109/TII.2018.2858281
Carazas FJG, de Souza GFM (2012) Reliability analysis of gas turbine. Thermal power plant performance analysis. Springer, New York, pp 189–220
Carter TJ (2005) Common failures in gas turbine blades. Eng Fail Anal. https://doi.org/10.1016/j.engfailanal.2004.07.004
Chang Y, Chen G, Wu X, Ye J, Chen B, Xu L (2018) Failure probability analysis for emergency disconnect of deepwater drilling riser using Bayesian network. J Loss Prev Process Ind. https://doi.org/10.1016/j.jlp.2017.11.005
Dongiovanni DN, Iesmantas T (2016) Failure rate modeling using fault tree analysis and Bayesian network: DEMO pulsed operation turbine study case. Fusion Eng Des. https://doi.org/10.1016/j.fusengdes.2016.02.036
Halpern YY, Sontag D (2013) Unsupervised learning of noisy-or Bayesian networks. arXiv preprint arXiv:1309.6834
Hamza Z, Abdallah T (2015) Mapping fault tree into Bayesian network in safety analysis of process system. In: 2015 4th international conference on electrical engineering, ICEE 2015. https://doi.org/10.1109/INTEE.2015.7416862
Jahromi SAJ, Goudarzi MM, Nazarboland A (2008) Failure analysis of GE-F9 gas turbine journal bearings. Iran J Sci Technol 32(B1):61
Jones B, Jenkinson I, Yang Z, Wang J (2010) The use of Bayesian network modeling for maintenance planning in a manufacturing industry. Reliab Eng Syst Saf 95:267–277
Joshi P, Bikkina P, Wang Q (2016) Consequence analysis of accidental release of supercritical carbon dioxide from high-pressure pipelines. Int J Greenh Gas Control. https://doi.org/10.1016/j.ijggc.2016.10.010
Kargarnejad S, Abbasi-Chianeh V (2014) Failure analysis of a burner ring made of 20Cr32Ni1Nb alloy in a gas turbine combustion chamber. Case Stud Eng Fail Anal. https://doi.org/10.1016/j.csefa.2014.07.001
Khakzad N, Khan F, Amyotte P (2011) Safety analysis in process facilities: comparison of fault tree and Bayesian network approaches. Reliab Eng Syst Saf. https://doi.org/10.1016/j.ress.2011.03.012
Kjærulff UB, Madsen AL (2008) Bayesian networks and influence diagrams. Inf Sci Stat. https://doi.org/10.1007/978-0-387-74101-7
Kolagar AM, Tabrizi N, Cheraghzadeh M, Shahriari MS (2017) Failure analysis of gas turbine first stage blade made of a nickel-based superalloy. Case Stud Eng Fail Anal. https://doi.org/10.1016/j.csefa.2017.04.002
Kuo W, Zhu X (2012) Some recent advances on importance measures in reliability. IEEE Trans Reliab. https://doi.org/10.1109/TR.2012.2194196
Lee YK, Mavris DN, Volovoi VV, Yuan M, Fisher T (2010) A fault diagnosis method for industrial gas turbines using Bayesian data analysis. J Eng Gas Turbines Power 132:41602
Liu Z, Liu Y, Wu X, Cai B (2018) Risk analysis of subsea blowout preventer by mapping GO models into Bayesian networks. J Loss Prev Process Ind. https://doi.org/10.1016/j.jlp.2018.01.014
Mishra RK, Thomas J, Srinivasan K, Nandi V, Bhatt RR (2017) Failure analysis of an un-cooled turbine blade in an aero gas turbine engine. Eng Fail Anal. https://doi.org/10.1016/j.engfailanal.2017.05.042
Montani S, Portinale L, Bobbio A, Codetta-Raiteri D (2008) Radyban: a tool for reliability analysis of dynamic fault trees through conversion into dynamic Bayesian networks. Reliab Eng Syst Saf. https://doi.org/10.1016/j.ress.2007.03.013
Moon H, Kim KM, Jeon YH, Shin S, Park JS, Cho HH (2015) Effect of thermal stress on creep lifetime for a gas turbine combustion liner. Eng Fail Anal. https://doi.org/10.1016/j.engfailanal.2014.10.004
Noroozian A, Kazemzadeh RB, Niaki STA, Zio E (2018) System risk importance analysis using Bayesian networks. Int J Reliab Qual Saf Eng. https://doi.org/10.1142/S0218539318500043
Oyedepo SO, Fagbenle RO, Adefila SS (2015) Assessment of performance indices of selected gas turbine power plants in Nigeria. Energy Sci Eng. https://doi.org/10.1002/ese3.61
Rafsanjani HM, Nasab AR (2012) Risk assessment of failure modes of gas diffuser liner of V94.2 siemens gas turbine by FMEA method. J Phys Conf Ser 364(1):012137. https://doi.org/10.1088/1742-6596/364/1/012137
Salehnasab B, Poursaeidi E, Mortazavi SA, Farokhian GH (2016) Hot corrosion failure in the first stage nozzle of a gas turbine engine. Eng Fail Anal. https://doi.org/10.1016/j.engfailanal.2015.11.057
Tanaka H, Fan LT, Lai FS, Toguchi K (1983) Fault-tree analysis by fuzzy probability. IEEE Trans Reliab. https://doi.org/10.1109/TR.1983.5221727
Wang G, Xu T, Tang T, Yuan T, Wang H (2017a) A Bayesian network model for prediction of weather-related failures in railway turnout systems. Expert Syst Appl. https://doi.org/10.1016/j.eswa.2016.10.011
Wang W, Shen K, Wang B, Dong C, Khan F, Wang Q (2017b) Failure probability analysis of the urban buried gas pipelines using Bayesian networks. Process Saf Environ Prot. https://doi.org/10.1016/j.psep.2017.08.040
Weber P, Medina-Oliva G, Simon C, Iung B (2012) Overview on Bayesian networks applications for dependability, risk analysis and maintenance areas. Eng Appl Artif Intell. https://doi.org/10.1016/j.engappai.2010.06.002
Yang H, Xu H (2011) Reliability analysis of Gas turbine based on the failure mode and effect analysis. In: Power and energy engineering conference (APPEEC), 2011 Asia-Pacific. IEEE, pp 1–4
Yontay P, Pan R (2016) A computational Bayesian approach to dependency assessment in system reliability. Reliab Eng Syst Saf 152:104–114
Zhang SZ, Yu H, Ding H, Yang NH, Wang XK (2003) An application of online learning algorithm for Bayesian network parameter. In: 2003 international conference on machine learning and cybernetics, vol 1, pp 153–156. https://doi.org/10.1109/ICMLC.2003.1264461
Zhao Y, Xiao F, Wang S (2013) An intelligent chiller fault detection and diagnosis methodology using Bayesian belief network. Energy Build. https://doi.org/10.1016/j.enbuild.2012.11.007
Zhong T, Brenda M (2007) Developing complete conditional probability tables from fractional data for Bayesian belief networks. J Comput Civ Eng. https://doi.org/10.1061/(ASCE)0887-3801(2007)21:4(265)
Author information
Authors and Affiliations
Corresponding author
Additional information
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
About this article
Cite this article
Mirhosseini, A.M., Adib Nazari, S., Maghsoud Pour, A. et al. Probabilistic failure analysis of hot gas path in a heavy-duty gas turbine using Bayesian networks. Int J Syst Assur Eng Manag 10, 1173–1185 (2019). https://doi.org/10.1007/s13198-019-00848-z
Received:
Revised:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s13198-019-00848-z