# A Hybrid Hidden Markov Model for Pipeline Leakage Detection
## 1 Introduction  
Damage detection has been widely studied especially in the pipeline to avoid enormous economic loss and environmental disasters [1]. Pipeline leakage detection is an essential component of risk management as it allows operators to respond in time to leaks and to prevent further escalation of incidents. In the last few decades, many pipeline leakage detection techniques have been used to monitor damages for the offshore oil and gas industry. Existing leakage detection techniques are acoustic emission [2], fiber optic sensing [3], negative pressure wave (NPW) detection [4], etc. The extracted signals from these techniques can be analyzed with statistical models to monitor the damages. For instance, statistical models, such as support vector machine (SVM) [5; 6; 7] and hidden Markov model (HMM) [8; 9], have been adopted to facilitate and enhance the pipeline detection process utilizing the various extracted signals from the detection techniques. [PERSON] et al. evaluated a deep neural network for spectrum recognition of underwater targets [10]. [PERSON] et al. compared detection performance between statistic models on the boiler tube [11]. However, these studies only focus on classification without considering the sequential changes of damage states. Furthermore, to exploit the feature information in signals, Gaussian mixture model-hidden Markov model (GMM-HMM) has been implemented to improve the pipeline damage detection performance [12; 13]. In our previous study, GMM-HMM was applied for pipeline leakage and crack depth detection [14]. Each hidden state has a probability distribution over the possible leakage states where the probability distribution matrix is initialized by a Gaussian mixture model(GMM). An iterative training process through the Baum-Welch algorithm is applied to get the optimized parameters of the HMM. The posterior probabilities of leakage states obtained by HMM is used to obtain a probabilistic evaluation of the pipeline damage. The proposed GMM-HMM can recognize the crack depth by using the guided wave and leakage location of the pipeline by using the negative pressure wave. Signals were collected by Lead Zirconate Titanate (PZT) transducers. Different crack depths and different leakage locations of pipeline were defined as different states in the hidden Markov model. That work successfully answered our research questions, i.e., whether the pipeline has a leak, where the leakage location is and how deep the crack is. In addition, the GMM-HMM method [14] has the ability to detect the sequential changes of states. However, GMM-HMM becomes less efficient for pipeline leakage detection due to the demands of a massive amount of data to identify parameters of Gaussian mixtures. GMM typically has a large number of Gaussians when there are many hidden states. The GMM with independently parameterized means from such states may result in those Gaussians being highly localized and thus may result in such models only performing local generalization. This situation will become worse when changing environmental factors are considered. Therefore, in the real-world application, the changing environment and time-varying operational conditions make the reliability of pipeline leakage detection facing the challenge. To overcome this predicament, one of the techniques is to replace the GMM with reliable models that can tackle with a massive amount of data and achieve higher accuracy. With the surging of deep learning, neural networks can model multiple events and learn richer representations that have the potential to learn better models of nonlinear data [15; 16; 17]. With multiple layers, deep neural networks (DNNs) [18; 19] perform well on decision boundary and feature engineering problems by using a massive amount of data [20]. In recent years, a deep neural network hidden Markov model (DNN-HMM) has been proposed as a novel hybrid architecture and has been widely used on acoustic learning [21; 22; 23].