INTELLIGENT IOT ENABLED PIPELINE LEAKAGE DETECTION AND MONITORING SYSTEM USING DEEP METAHEURISTIC LEARNING ALGORITHM

Abstract

Pipeline networks are the safest transportation of oil and gas-related products that may be susceptible to failures. These kinds of failures are due to the reason of manufacturing defects, degradation of the material, environmental defects, and interference of the third party. These issues are addressed through the Internet of Things (IoT) based solutions and give promising outcomes in predicting the failures and monitoring. The IoT can combine the sensor technologies to monitor the gas flow, pressure, temperature, condition of the compressor, concentration, and other related features inside the pipeline. The leaks in the pipe are measured as a pin-sized hole that is detected by the IoT sensor with advanced technologies such as deep learning. This paper proposed deep learning with a meta-heuristic approach-based model to detect leaks in pipelines through IoT sensors. The Deep Learning model called Deep Auto Encoder Neural network (DAENN) is the unsupervised model that can accurately classify the leaking and non-leaking pipeline conditions. The detection accuracy is further enhanced with the Bat Optimization algorithm (BOA) which obtained improved accuracy while leaks occur in the pipeline inside the sensor monitoring area. This observation deploys the monitoring sensors to cover the mentioned monitoring area. The proposed model can increase the system’s leak detection reliability and reduces the false alarm rate. The trained DAENN-BOA with its leak detection strategy is tested against a simulated dataset that secured promising results with an accuracy of 98.9% and a reduced error rate of 0.09.