Preventive and Predictive CNN Based Solution for Pipeline Leak, Blockage and Corrosion Detection

Abstract

Using Artificial Intelligence (AI) in Internet of Things (IoT) environments has shown great potential to transform many industries. As oil and gas industries continue to push for digital transformation, safety critical applications such as pipeline leak detection, corrosion and blockage detection could also benefit from the adoption of AI and IoT. In our day-to-day life, we come across pipeline accidents and their after-effects. Structural deterioration of pipes in urban distribution network has presented great challenges to the utilities all over the world. When the deterioration exceeds its resiliency, the pipes leak or burst, leading to significant socio-economic overhead. The harsh operating environment, extreme temperature and weather conditions increases the potential for corrosion induced damages to the pipelines. The extreme conditions where the pipelines are located also make it difficult to rely on human operators to physically monitor these pipelines and respond to observed leaks. We propose a preventive and predictive AI-based solution that can continuously monitor the health of a complex pipeline infrastructure. It is a complete solution where pipelines will be scrutinized for blockage, leakage, corrosion, and defects. We propose a novel approach in which video images from IoT cameras installed across various locations on the pipelines are continuously analyzed and a convolutional neural network model is implemented for detecting leaks from the pipeline. Flow sensors will be mounted on the hardware to measure the fluid flow rate. This approach is proposed to deliver greater benefits in terms of accuracy and efficiency.