15 Jun Risk assessment
Accidents occur because of a plethora of causation agents and conditions, which interact with one other in complex ways, leading to events that lead to or combine with other such events and ultimately result in accidents. The combined likelihood of the various causation factors, whether related to planning failure, technological failure, human failure, or a combination of these factors, is used in risk assessment approaches to identify both the probability of occurrence of accidents and their consequences. These are also used in the development of strategies to minimize the chances of accident occurrence and to frame emergency preparedness strategies (Khan and Abbasi, 1998).
Probabilistic risk assessment (PRA) processes are comprehensive, structured and logical methods widely used for safety purposes. PRA approaches include, but not limited to Fault Tree Analysis (FTA), Failure Mode and Effects Analysis (FMEA), and Event Tree Analysis (ETA). In conventional PRA, failure data about components is required for the purposes of quantitative analysis. In practice, it is not always possible to fully obtain this data due to unavailability of primary observations and consequent scarcity of statistical data about the failure of components. To handle such situations, fuzzy set theory has been successfully used in novel PRA approaches for safety and reliability evaluation under conditions of uncertainty (Kabir and Papadopoulos, 2018).
Different probabilistic risk assessment (PRA) methods have been used to evaluate system safety and reliability. Fault tree analysis (FTA) is one of the most widely used PRA approaches to estimate system safety and reliability. In fault trees, the logical connections between faults and their causes are represented graphically. Failure Mode and Effects Analysis (FMEA), Event Tree Analysis (ETA), Markov chains, Bayesian networks, and Petri nets are some of the other approaches that are used for safety and reliability evaluation of systems. In all the PRA approaches, the system failure probability is evaluated as a function of the failure probability of the system components (e.g. the basic events). Therefore, the applicability of these analysis methods for evaluating system safety and reliability is largely dependent on the availability of the components’ lifetime data. Any uncertainties raised in the components failure probability will consequently propagate it to the results. On the other hand, unavailability of failure data would introduce degrees of uncertainty into the analysis results (Kabir and Papadopoulos, 2018).
- Kabir, S., Papadopoulos, Y. (2018). “A review of applications of fuzzy sets to safety and reliability engineering”. International Journal of Approximate Reasoning, 100, 29–55.
- Khan, F.I., Abbasi, S. A. (1998). “Multivariate Hazard Identification and Ranking System”. Process Safety Progress 17 (3), 157–170.
- Srinivasan, R., Srinivasan, B., Umair Iqba, M., Nemet, A., Kravanja, Z. (2019). “Recent developments towards enhancing process safety: Inherent safety and cognitive engineering”. Computers and Chemical Engineering, Volume 128, Pages 364-383.