Abstract
Failure analysis is vital to prevent potential incidents in chemical process industries. The varieties of different failure analysis methods such as fault tree analysis (FTA) help assessors to optimize the amount of risk by providing corresponding corrective actions. However, such conventional failure analysis techniques still suffer from several shortages. As an example, availability of failure data in some cases is rare and besides they cannot be much more effective in dynamic structure. In this paper, a new framework based on probabilistic failure analysis using an integration of FTA and Petri-nets are proposed to provide ability in dynamic structure. Fuzzy logic is also used to deal with uncertainty conditions when there is a lack of information. A real case study of kick in chemical process industry is surveyed to show the effectiveness and efficiency of the proposed model.
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Yazdi, M., Darvishmotevali, M. (2019). Fuzzy-Based Failure Diagnostic Analysis in a Chemical Process Industry. In: Aliev, R., Kacprzyk, J., Pedrycz, W., Jamshidi, M., Sadikoglu, F. (eds) 13th International Conference on Theory and Application of Fuzzy Systems and Soft Computing — ICAFS-2018. ICAFS 2018. Advances in Intelligent Systems and Computing, vol 896. Springer, Cham. https://doi.org/10.1007/978-3-030-04164-9_95
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DOI: https://doi.org/10.1007/978-3-030-04164-9_95
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