A Hybrid Hidden Markov Model for Pipeline Leakage Detection
A Hybrid Hidden Markov Model for Pipeline Leakage Detection
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DOI：
10.3390/app11073138
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发表时间：
2021-04
期刊：
影响因子：
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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
提出了一种基于深度神经网络隐马尔可夫模型（DNN-HMM）的管道泄漏位置检测方法。将一条长管道划分为若干段，泄漏发生在不同的段，定义为隐马尔可夫模型（HMM）的不同状态。混合HMM，即，DNN-HMM由多层深度神经网络（DNN）组成，以利用非线性数据。DNN通过使用深度信念网络（DBN）来初始化。DBN是通过堆叠自上而下的限制性玻尔兹曼机（RBM）而构建的预训练模型，该限制性玻尔兹曼机（RBM）计算HMM而不是高斯混合模型（GMM）的发射概率。基于不同数量的状态，使用高斯混合模型-隐马尔可夫模型（GMM-HMM）和DNN-HMM的比较研究。用微F1值来衡量检测到的状态序列与实际状态序列之间的测试性能的准确性。当管道分为三段时，GMM-HMM方法的微观F1分数接近0.94，DNN-HMM方法的微观F1分数接近0.95。在将流水线划分为五段的实验中，GMM-HMM的微观F1分数为0.69，而DNN-HMM方法接近0.96。实验结果表明，与GMM-HMM方法相比，DNN-HMM方法能够更好地学习非线性数据的模型，并取得更好的性能。
In this paper, a deep neural network hidden Markov model (DNN-HMM) is proposed to detect pipeline leakage location. A long pipeline is divided into several sections and the leakage occurs in different section that is defined as different state of hidden Markov model (HMM). The hybrid HMM, i.e., DNN-HMM, consists of a deep neural network (DNN) with multiple layers to exploit the non-linear data. The DNN is initialized by using a deep belief network (DBN). The DBN is a pre-trained model built by stacking top-down restricted Boltzmann machines (RBM) that compute the emission probabilities for the HMM instead of Gaussian mixture model (GMM). Two comparative studies based on different numbers of states using Gaussian mixture model-hidden Markov model (GMM-HMM) and DNN-HMM are performed. The accuracy of the testing performance between detected state sequence and actual state sequence is measured by micro F1 score. The micro F1 score approaches 0.94 for GMM-HMM method and it is close to 0.95 for DNN-HMM method when the pipeline is divided into three sections. In the experiment that divides the pipeline as five sections, the micro F1 score for GMM-HMM is 0.69, while it approaches 0.96 with DNN-HMM method. The results demonstrate that the DNN-HMM can learn a better model of non-linear data and achieve better performance compared to GMM-HMM method.