Decision Support System for Inspection and Maintenance: A Case Study of Oil Pipelines

TLDR

Current pipeline health monitoring requires periodic inspection of entire pipelines, which is inefficient. The DSS applies the analytic hierarchy process to rank risk factors for each segment, estimates their probability and consequence cost, and aggregates these to compute a cumulative failure impact. The model reduces inspection costs by objectively prioritizing segments, allocating budgets, and guiding labor and insurance decisions, while also informing system‑wide inspection policies and design recommendations for new pipelines.

Abstract

The existing method of pipeline health monitoring, which requires an entire pipeline to be inspected periodically, is unproductive. A risk-based decision support system (DSS) that reduces the amount of time spent on inspection has been presented. The risk-based DSS uses the analytic hierarchy process (AHP), a multiple attribute decision-making technique, to identify the factors that influence failure on specific segments and analyzes their effects by determining probability of occurrence of these risk factors. The severity of failure is determined through consequence analysis. From this, the effect of a failure caused by each risk factor can be established in terms of cost and the cumulative effect of failure is determined through probability analysis. The model optimizes the cost of pipeline operations by reducing subjectivity in selecting a specific inspection method, identifying and prioritizing the right pipeline segment for inspection and maintenance, deriving budget allocation, providing guidance to deploy the right mix labor for inspection and maintenance, planning emergency preparation, and deriving logical insurance plan. The proposed methodology also helps derive inspection and maintenance policy for the entire pipeline system, suggest design, operational philosophy, and construction methodology for new pipelines.