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Erschienen in:

2021 | OriginalPaper | Buchkapitel

Probabilistic and Fuzzy Models for Risk Analysis of Processing and Manufacturing System

verfasst von : Priyank Srivastava, Navnidh, Sarthak Bali, Rishabh Gupta, Rajendra Kumar Shukla, Ruchika Gupta, Dinesh Khanduja, Melfi Alrasheedi, Rakesh Kumar Phanden

Erschienen in: Advances in Industrial and Production Engineering

Verlag: Springer Singapore

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Abstract

Risk is defined as the chance of failures or a state occurrence of the possible loss. The risk of one can be a benefit to others. To survive in today's competitive market situation, proper risk management is essential. In the present study, various models, that is, probabilistic and fuzzy models used for risk analysis in processing and manufacturing systems, have been discussed. This article critically reviews the use of probabilistic and fuzzy models in context with the type of publications, area of application, region-specific details, citations results to answer the research questions framed. This main contribution of this article is the examination of the current state of the art for risk analysis. The findings from this article will be helpful for risk managers and analysts, respectively, for proper understanding, identification, analysis, and management of risk.

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Panchal, D., Kumar, D.: Risk analysis of compressor house unit in thermal power plant using integrated fuzzy FMEA and GRA approach. Int. J. Ind. Syst. Eng. 25(2), 228–250 (2017)

Panchal, D., Mangla, S.K., Tyagi, M., Ram, M.: Risk analysis for clean and sustainable production in a urea fertilizer industry. Int. J. Qual. Reliab. Manag. 35(7), 1459–1476 (2018)CrossRef

Panchal, D., Srivastava, P.: Qualitative analysis of CNG dispensing system using fuzzy FMEA–GRA integrated approach. Int. J. Syst. Assurance Eng. Manag. 10(1), 44–56 (2019)CrossRef

Srivastava, P., et al.: Risk analysis of CNG dispensing unit by fuzzy digraph matrix and dempster-shafer approach. Int. J. Product. Qual. Manag. 28(2), 228–255 (2019)CrossRef

Srivastava, P., Khanduja, D., Ganesan, S.: Fuzzy methodology application for risk analysis of mechanical system in process industry. Int. J. Syst. Assurance Eng. Manag. (2019)

Sharma, P., Sharma, R.K.: System failure behaviour and maintenance decision making using, RCA, FMEA and FM. J. Qual. Maintenance Eng. 16(1), 64–88 (2010)

Chin, K.-S., Wang, Y.-M., Ka Kwai Poon, G., Yang, J.-B.: Failure mode and effects analysis using a group-based evidential reasoning approach. Comput. Oper. Res. 36(6), 1768–1779 (2009)

Pat-Cornell, M.E.: Uncertainties in risk analysis: six levels of treatment. Reliab. Eng. Syst. Saf. 54, 95–111 (1996)

David, D.A.M., Liberati, A., Tetzlaff, J., Altman, D.G., Altman, D., Antes, G.: Preferred reporting items for systematic reviews and meta-analyses: the PRISMA statement. J. Chin. Integr. Med. 7(9), 889–896 (2009)

10.

Ilangkumaran, M., Karthikeyan, M., Ramachandran, T., Boopathiraja, M., Kirubakaran, B.: Risk analysis and warning rate of hot environment for foundry industry using hybrid MCDM technique. Saf. Sci. 72, 133–143 (2015)CrossRef

11.

Aneziris, O.N., Papazoglou, I.A., Konstantinidou, M., Nivolianitou, Z.: Integrated risk assessment for LNG terminals. J. Loss Prevent. Process Industr. 28(SI), 23–35 (2014)

12.

Sano, K., Koshiba, Y., Ohtani, H.: Risk assessment and risk reduction of an acrylonitrile production plant. J. Loss Prevent. Process Industr. 63 (2020)

13.

Khaloo, S.S., Saeedi, R., Sanjari, A.: Environmental risk assessment and corrective measures for the metal rolling industry. Environm. Monitor. Assess. 191(9) (2019)

14.

Animah, I., Shafiee, M.: Application of risk analysis in the liquefied natural gas (LNG) sector: an overview. J. Loss Prevent. Process Industr. 63 (2020)

15.

Dadkhah, S.M., Golbabaei, F., Malakootikhah, J., Mohamadfam, I.: Appropriateness criteria for choosing proper risk assessment model of nanomaterial manufacturing processes. Asian J. Chem. 24(8), 3719–3723 (2012)

16.

Song, Y.H., Yu, H.Q., Lv, W.: Risk analysis of dairy safety incidents in China. Food Control 92, 63–71 (2018)CrossRef

17.

Luis Fuentes-Bargues, J., Carmen Gonzalez-Cruz, M., Gonzalez-Gaya, C., Piedad Baixauli-Perez, M.: Risk analysis of a fuel storage terminal using HAZOP and FTA. Int. J. Environ. Res. Public Health 14(7) (2017)

18.

Komal: Fuzzy fault tree analysis for patient safety risk modeling in healthcare under uncertainty. Appl. Soft Comput. 37, 942–951 (2015)

19.

Kou, G., Peng, Y., Wang, G.: Evaluation of clustering algorithms for financial risk analysis using MCDM methods. Inf. Sci. 275, 1–12 (2014)CrossRef

20.

Khakzad, N., Khan, F., Amyotte, P.: Quantitative risk analysis of offshore drilling operations: a Bayesian approach. Saf. Sci. 57, 108–117 (2013)CrossRef

21.

Cheliyan, A.S., Bhattacharyya, S.K.: Fuzzy fault tree analysis of oil and gas leakage in subsea production systems. J. Ocean Eng. Sci. 3(1), 38–48 (2018)CrossRef

22.

Chang, Y., et al.: Comprehensive risk assessment of deepwater drilling riser using fuzzy Petri net model. Process Saf. Environ. Protect. 117, 483–497 (2018)CrossRef

23.

Khan, F.I., Husain, T.: Risk assessment and safety evaluation using probabilistic fault tree analysis. Human Ecol. Risk Assess. 7(7), 1909–1927 (2001)CrossRef

24.

Yasli, F., Bolat, B.: A risk analysis model for mining accidents using a fuzzy approach based on fault tree analysis. J. Enterprise Inf. Manag. 31(4), 577–594 (2018)

25.

Burgazzi, L.: Probabilistic safety analysis of an accelerator—lithium target based experimental facility. Nuclear Eng. Des. 236(12), 1264–1274 (2006)CrossRef

26.

Barak, S., Dahooei, J.H.: A novel hybrid fuzzy DEA-Fuzzy MADM method for airlines safety evaluation. J. air Transp. Manag. 73, 134–149 (2018)CrossRef

27.

Wang, Y.F., Chang, C.T.: Petri-net-based deductive reasoning strategy for fault identification in batch processes. Ind. Eng. Chem. Res. 43(11), 2704–2720 (2004)CrossRef

Titel: Probabilistic and Fuzzy Models for Risk Analysis of Processing and Manufacturing System
verfasst von: Priyank Srivastava
Navnidh
Sarthak Bali
Rishabh Gupta
Rajendra Kumar Shukla
Ruchika Gupta
Dinesh Khanduja
Melfi Alrasheedi
Rakesh Kumar Phanden
Verlag: Springer Singapore
Buch: Advances in Industrial and Production Engineering
Print ISBN: 978-981-334-319-1

Electronic ISBN: 978-981-334-320-7

Copyright-Jahr: 2021
DOI: https://doi.org/10.1007/978-981-33-4320-7_7

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