A Hybrid Hidden Markov Model for Pipeline Leakage Detection

Abstract

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.

Research on Pig Sound Recognition Based on Deep Neural Network and Hidden Markov Models

“…DNN-HMM consists of DNN, HMM and a priori probability distribution. Due to the phoneme-binding structure shared by DNN-HMM and GMM-HMM, a GMM-HMM system needs to be trained before training the DNN-HMM model [ 30 ]. DNN training tagging is generated by the GMM-HMM system and the Viterbi algorithm, and the quality of tagging will affect the performance of the DNN system, so the initial training model of GMM-HMM is very important.…”

Study of a Machine Vision Approach to Leak Monitoring of a Marine System

“…Akhand Rai et al [8] proposed a health index method for pipeline leak detection based on multiscale analysis, a Kolmogorov-Smirnov test, and a Gaussian mixture model. Zhang et al [9] used a hidden Markov model based on deep neural networks to detect pipe leak locations. Software-based approaches offer the advantages of high flexibility, low cost, rapid iteration and improvement, wide application, and ease of integration and interaction.…”

Leakage simulation and acoustic characteristics based on acoustic logging by ultrasonic detection

“…It often occurs in practical engineering applications, not only causing energy loss and environmental pollution, but also threatening the safety of residents (Mahmutoglu and Turk, 2019;Zhang and Weng, 2020;Rai et al, 2021). Therefore, casing leakage detection is a crucial part of well integrity management (Datta et al, 2016;Ning et al, 2021;Zhang et al, 2021c). To avoid accidents due to oil and gas pipeline leakage, the exact location and quantity of leakage must be identified (Lalitha et al, 2020;Jahanian et al, 2021).…”