Dynamic Risk Assessment Based on Statistical Failure Data and Condition-Monitoring Degradation Data

Abstract

Traditional quantitative risk assessment methods (e.g., event tree analysis) are static in nature, i.e., the risk indexes are assessed before operation, which prevents capturing time-dependent variations as the components and systems operate, age, fail, are repaired and changed. To address this issue, we develop a dynamic risk assessment (DRA) method that allows online estimation of risk indexes using data collected during operation. Two types of data are considered: statistical failure data, which refer to the counts of accidents or near misses from similar systems and condition-monitoring data, which come from online monitoring the degradation of the target system of interest. For this, a hierarchical Bayesian model is developed to compute the reliability of the safety barriers and a Bayesian updating algorithm, which integrates particle filtering (PF) with Markov Chain Monte Carlo, is developed to update the reliability evaluations based on both the statistical and condition-monitoring data. The updated safety barriers reliabilities, are, then, used in an event tree (ET) for consequence analysis and the risk indexes are updated accordingly. A case study on a high-flow safety system is conducted to demonstrate the developed methods. A comparison to the DRA method which only uses statistical failure data shows that by introducing condition-monitoring data on the system degradation process, it is possible to capture the system-specific characteristics, and, therefore, provide a more complete and accurate description of the risk of the target system.

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