Bolt Looseness Detection in Flanged Pipes Using Parametric Modeling

Abstract

Detecting bolt looseness in flanged pipes at an early stage is critical for ensuring safety in the oil and gas industry. Failure to identify such looseness can lead to severe incidents such as leaks and explosions. One effective indirect method for detecting looseness is through vibration analysis of the structures connected by bolted joints. Changes in vibration parameters can indicate looseness, which affects the bolted joints’ structural stiffness. This study presents a novel parametric modeling algorithm for detecting bolt looseness in flanged pipes. Autoregressive (AR) model parameters serve as the feature vector for a Mahalanobis distance–based indicator, facilitating accurate looseness detection. Validation was conducted using AR and time-varying autoregressive (TAR) models adapted to the stationary and nonstationary vibration signals of flanged pipes, respectively. The structure was excited using white noise (stationary state) and a moving mass inside the pipe (nonstationary state) to ensure practical applicability. The results demonstrate the method’s effectiveness in detecting flange looseness at early stages using an output-only approach. The developed vibration-based method for detecting flange bolt looseness offers significant practical applications, especially in pipeline integrity management. A novel application involves using a pipeline inspection gauge (PIG) as a moving mass, leveraging its movement within pipes for dynamic excitation similar to the study’s approach. Integrating this method with pigging operations enables real-time monitoring of pipeline health. Vibration responses induced by the PIG were analyzed using TAR models and Mahalanobis distance–based indicators to detect various structural issues such as flange bolt looseness, weld cracks, and erosion- and corrosion-induced material loss. However, further investigation is needed to integrate the hardware (such as accelerometers) with the PIG itself, and this is currently under study. Practically, this approach offers: •Early detection of structural defects: identifies issues like flange bolt looseness early, facilitating timely maintenance to prevent failures.•Continuous monitoring: utilizes existing pigging routines for cost-effective, ongoing structural health monitoring.•Enhanced pipeline safety: early defect detection enhances overall safety and reliability.•Improved maintenance scheduling: early detection allows for better planning, reducing downtime and costs.•Applicability to various pipeline systems: adaptable to different pipelines, enhancing its utility across the industry. In conclusion, integrating this vibration-based method with pigging operations offers an efficient solution for maintaining pipeline integrity. By providing early defect warnings, it significantly boosts safety, reliability, and operational efficiency.