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 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 an offshore environment. In this paper, a Gaussian mixture model-hidden Markov model (GMM-HMM) method is proposed to process PZT transducers’ outputs for detecting 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 (HMMs). One time-domain damage index and one frequency domain damage index are extracted from signals collected from PZT transducers, then extracted indices are formed as observation emissions in the HMM. The observation probability distribution matrix in HMM is initialized by a Gaussian mixture model (GMM) to address signal uncertainties. After the HMM parameter initialization, an iterative training process through the Baum–Welch algorithm is applied to get the optimized parameters of the GMM-HMM. Leakage location or crack depth is decided by the maximum posterior probability from the trained model. Two different experimental settings and results 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.

石油和天然气管道的泄漏不仅导致巨大的经济损失，而且还导致环境灾难。如何检测包括泄漏和裂缝在内的管道损伤已经引起了很多研究关注。一种有前途的泄漏检测方法是使用锆钛酸铅（PZT）换能器在发生泄漏时检测负压波。PZT换能器还可以生成和检测导波，以进行裂纹检测。然而，由于环境干扰，例如海上环境中的石油和天然气管道，可能不容易检测到负压波或导向应力波。在本文中，提出了一种高斯混合模型-隐马尔可夫模型（GMM-HMM）方法来处理PZT换能器的输出，以在变化的环境和时变的工作条件下检测管道泄漏和裂纹深度。在隐马尔可夫模型（HMM）中，不同截面的泄漏或裂纹深度被视为不同的状态。从PZT换能器收集的信号中提取一个时域损伤指数和一个频域损伤指数，然后将提取的指数形成为HMM中的观测发射。HMM中的观察概率分布矩阵由高斯混合模型（GMM）初始化，以解决信号不确定性问题。HMM参数初始化后，通过Baum-Welch算法进行迭代训练，以获取GMM-HMM的优化参数。泄漏位置或裂纹深度由训练模型的最大后验概率决定。两种不同的实验设置和结果表明，GMM-HMM方法可以识别管道的裂缝深度和泄漏，例如是否存在泄漏，泄漏在哪里。