Human Reliability Analysis (HRA) Using Standardized Plant Analysis Risk-Human (SPAR-H) and Bayesian Network (BN) for Pipeline Inspection Gauges (PIG) Operation: A Case Study in a Gas Transmission Plant

Abstract

Background: Pigging operation is one of the maintenance activities that is used to check pipeline functionality in operational conditions using a PIG device and high pressure of liquid/gas, which is potentially hazardous. Objectives: The present study aimed at customizing SPAR-H methodology for the pigging operation using Bayesian networks (BNs). It also aimed at identifying and analyzing human errors in pigging operation in a gas transmission company. Methods: The current article was composed of two main steps. In the first step, the SPAR-H BN model was developed using expert-elicited prior probabilities of pigging operation applied to Bayesian network. In this step, CPTs of PSF nodes are constructed using prior probabilities, which are achieved from expert opinion. The CPT of error node is developed using coding process of SPAR-H formula in a simulation node. In the second step, a descriptive study was carried out to estimate the probability of human errors in pigging operation in a gas transmission plant in Iran. First, hierarchical task analysis (HTA) was conducted by walking through the pigging operation and interviews with workers. Next, the SPAR-H BN model was utilized for estimation of human error probability. Results: The developed model was tested on the pigging operation subtasks. In the considered case study, the mean probability of human error was estimated as 0.184. The highest probability of human error was related to “opening the kicker valve for enhancing pressure” subtask. Conclusions: The BNs were helpful to adapt the SPAR-H methodology to the pigging operation using dedicated prior probabilities. Beside that, the probabilities of human error can be updated taking into account the more realistic operational and environmental conditions.