# Pipeline Leakage localization Based on FBG Hoop Strain Sensors Combined with BP Neural Network
## 1 Introduction  
Pipelines are widely used for transportation or conveying fluids from one place to another, for which automotive transportation is often dangerous or impractical. Pipeline leaks are among the most difficult problems causing both loss of product, as well as serious environmental pollution. Therefore, it is important to locate the leak point accurately to mitigate problems and avoid potentially crippling costs. Currently, some methods and systems for detecting pipeline leakage have been developed. Recent innovations on pipeline leak detection techniques can be classified into two categories: new sensors and new algorithms. From the aspect of the sensor, some novel sensing techniques are used to detect the pipeline fault due to higher sensitivity and reliability, like fiber optic sensing [1,2], wireless sensor networks [3,4] and the acoustic method [5,6]. Among these techniques, fiber optic sensors emerged as the most appropriate for long distance pipelines with no potential safety hazard compared with traditional electrical gauges. From the aspect of algorithms, the fluid transient state due to leakage occurrence is always analyzed, then the leak accidents can be identified, thus locating the leak point, in combination with signal processing techniques and mathematic analysis, including artificial neural network [7,8], support vector machine (SVM) [9,10], harmonic wavelet analysis [11],etc. Until now, no integrated solution for leakage detection combining both advantages of the fiber sensor and advanced algorithm has been proposed in the literature. Fiber optic sensors, with the advantages of immunity to electromagnetic interference, high accuracy and multiplexibility, are now widely used to detect pipeline fault since there are many probable dangers related to gas or liquid pipelines [12; 13]. A novel FBG hop strain sensor has been developed and encapsulated in our lab to measure the pipeline hoop strain variation induced by leaks or other pressure drop cases [14; 15]. This sensor provides a nondestructive testing (NDT) method for pipeline leakage detection and localization by installing multiple sensors along the pipeline. In addition, more information during the leakage transient flow process can be extracted so as to describe the leakage characteristics at different leak points or with different leak rates, which provides a potential approach to locate the leak point. The pipeline leakage detection and localization can be treated as a classification problem in which a leak fault condition is considered to be a sample. Different leakage scenarios can be identified by some machine learning algorithms, like k nearest neighbor (kNN) [16], support vector machine [10], multi-layer perceptron neural network (MLPNN) [9], etc. The fundamental idea of these methods is to extract features from flow, pressure and temperature measurements at the inlet and outlet of the pipeline and then to train the classifier. Until now, signals from the pipeline mid-section have not been utilized as feature extractions to enhance the reliability and accuracy of the classification algorithms because no additional sensors can be mounted along the pipeline. This paper provides a new pipeline leakage localization method integrating advanced FBG hop strain sensors and an effective BP neural network-based classification algorithm. The research work can be split into two branches. On the one hand, the FBG hop strain sensor developed in our previous work will be briefly demonstrated to show its performance in pipe pressure drop. On the other hand, the neural network system is trained by using multi-hoop strain characteristic values, then used to locate different leak points. The remainder of this paper is organized as follows: in the next part, the developed FBG hop strain sensor with enhanced sensitivity is introduced. Then, the theory of the neural network for pipeline leak localization is provided in detail.