Gaussian Mixture Marginal Distributions for Modelling Remaining Pipe Wall Thickness of Critical Water Mains in Non-Destructive Evaluation

TL;DR

This paper introduces a novel method combining Gaussian Mixture models with Gaussian Processes to accurately estimate the remaining wall thickness of water pipelines from partial inspection data, improving non-destructive evaluation efficiency.

Contribution

It proposes using Gaussian Mixture models to fit marginal distributions for better RWT prediction with Gaussian Processes in pipeline assessment.

Findings

Effective RWT estimation from partial scans

Validated on real-world pipeline data

Improved inference accuracy over traditional methods

Abstract

Rapidly estimating the remaining wall thickness (RWT) is paramount for the non-destructive condition assessment evaluation of large critical metallic pipelines. A robotic vehicle with embedded magnetism-based sensors has been developed to traverse the inside of a pipeline and conduct inspections at the location of a break. However its sensing speed is constrained by the magnetic principle of operation, thus slowing down the overall operation in seeking dense RWT mapping. To ameliorate this drawback, this work proposes the partial scanning of the pipe and then employing Gaussian Processes (GPs) to infer RWT at the unseen pipe sections. Since GP prediction assumes to have normally distributed input data - which does correspond with real RWT measurements - Gaussian mixture (GM) models are proven in this work as fitting marginal distributions to effectively capture the probability of any RWT value in the inspected data. The effectiveness of the proposed approach is extensively validated from real-world data collected in collaboration with a water utility from a cast iron water main pipeline in Sydney, Australia.