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Assessing Pipe Integrity Using Wireless Sensors Networks and Bayesian Network Modelling

The power generation industry is seeking solutions to prevent failures in high energy piping systems including main steam and hot reheat steam pipelines. A thorough review of prior failures in these systems has shown that 60-70% of all failures can be attributed to hanger and strain monitoring systems not performing within specification for extended periods of times (typically 1-5 years). The typical failure mode has been creep in seam welds. The consequence of failure for high energy piping can be severe and the effect of unexpected or unknown displacements cannot be quantified when estimating remaining life.In this case study we developed wireless LIDAR sensor networks that can be installed on either hangers or above insulation on these piping systems. The purpose of the sensor network is to protect life property and environment by reducing the likelihood catastrophic failures of high energy piping. The wireless sensors would communicate with the central control system at a power station and alert plant staff when the piping strain / bulk displacement is outside of design limits. A Bayesian network model was created which links the piping displacement to the remaining creep life predicted based on API 591 “API 579-1: Fitness-For-Service”. Therefore by knowing real-time pipe displacement measured using wireless sensor network the remaining creep life can be dynamically predicted though the Bayesian network model.

Product Number: 51319-13036-SG
Author: Shan Guan
Publication Date: 2019
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