Hidden Markov Models for Pipeline Damage Detection Using Piezoelectric Transducers

TL;DR

This paper proposes a GMM-HMM approach for detecting pipeline leaks and cracks using PZT transducers, effectively handling environmental noise and operational variability in offshore oil and gas pipelines.

Contribution

It introduces a novel GMM-HMM method for pipeline damage detection that accounts for changing environments and operational conditions, improving detection accuracy.

Findings

Successfully detects leakages and crack depths in laboratory tests.

Distinguishes different pipeline damage states effectively.

Handles environmental interference and operational variability.

Abstract

Oil and gas pipeline leakages lead to not only enormous economic loss but also environmental disasters. How to detect the pipeline damages including leakages and cracks has attracted much research attention. One of the promising leakage detection method is to use lead zirconate titanate (PZT) transducers to detect the negative pressure wave when leakage occurs. PZT transducers can generate and detect guided stress waves for crack detection also. However, the negative pressure waves or guided stress waves may not be easily detected with environmental interference, e.g., the oil and gas pipelines in offshore environment. In this paper, a Gaussian mixture model based hidden Markov model (GMM-HMM) method is proposed to detect the pipeline leakage and crack depth in changing environment and time-varying operational conditions. Leakages in different sections or crack depths are considered as different states in hidden Markov models (HMM). Laboratory experiments show that the GMM-HMM method can recognize the crack depth and leakage of pipeline such as whether there is a leakage, where the leakage is.