Hidden Markov models for pipeline damage detection using piezoelectric transducers
Hidden Markov models for pipeline damage detection using piezoelectric transducers
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DOI：
10.1007/s13349-021-00481-0
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发表时间：
2020-09
影响因子：
4.4
通讯作者：
Mingchi Zhang;Xuemin Chen;Wei Li
中科院分区：
文献类型：
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作者：
Mingchi Zhang;Xuemin Chen;Wei Li
油气管道泄漏不仅造成巨大的经济损失，而且还造成环境灾害。如何对管道泄漏、裂纹等损伤进行检测已成为国内外研究的热点。利用锆钛酸铅（PZT）传感器检测泄漏时产生的负压波是一种很有前途的泄漏检测方法。PZT换能器还可以产生和检测用于裂纹检测的导波。然而，在环境干扰的情况下可能不容易检测到负压波或引导应力波，例如，海上环境中的石油和天然气管道。本文提出了一种高斯混合模型-隐马尔可夫模型（GMM-HMM）的方法来处理PZT传感器的输出，用于在变化的环境和时变的运行条件下检测管道泄漏和裂纹深度。在隐马尔可夫模型中，不同截面或裂纹深度的泄漏被视为不同的状态。从压电传感器采集的信号中提取时域损伤指标和频域损伤指标，然后将提取的指标作为HMM中的观测辐射。HMM中的观测概率分布矩阵由高斯混合模型（GMM）初始化，以解决信号的不确定性。在HMM参数初始化之后，通过Baum-Welch算法的迭代训练过程来获得GMM-HMM的优化参数。通过训练模型的最大后验概率确定泄漏位置或裂纹深度。两种不同的实验设置和实验结果表明，GMM-HMM方法能够识别管道的裂纹深度和泄漏情况，如是否有泄漏、泄漏在哪里等。
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.