Visual inspection is a vital component of asset management that stands to benefit from automation. Using artificial intelligence to assist inspections can increase safety reduce access costs provide objective classification and integrate with digital asset management systems. The work presented herein investigates Deep Learning for automatic detection of corrosion on steel assets. A workflow is presented that spans from dataset creation to deployment highlighting the major hurdles and remaining work required. This process can be extended to other defects with a view to complete automation of visual inspection for asset management.

Estimating corrosion growth rate for underground pipelines is a non-linear multivariate problem. There are many potential confounding variables such as soil parameters cathodic protection AC/DC interference seasonal / climate conditions and proximity to unique geographic features such as wetlands or polluted environments. The work presented provides an approach for estimating underground corrosion growth rates using a dataset of observations from a North American pipeline operator. Extensive geospatial data is utilized that has been obtained from public and private sources and extrapolated using Inverse distance weighted (IDW) interpolation. This work presents a model using IDW to estimate parameters involving soil interference geography and climate factors for any location in North America.Using this data this work then presents several different machine learning approaches including Generalized Linear Models eXtreme Boosted Trees and Neural Networks. All three provide an accurate estimation for corrosion growth rates for an underground asset at any latitude and longitude pair in North America. Each method comes with potential benefits and pitfalls specifically; trade-offs between model accuracy and transparency. This work presents a framework for comparing geo-spatial and machine learning estimates. Findings and a framework are provided for owners to assess how to think machine learning on their own assets.

Pipelines have been the main transportation pattern of oil and gas because of their safety and economy, which are considered as the lifeline of offshore oil and gas transportation. With the booming development of offshore oil industry, the frequency of pipeline leakage is also increasing. Corrosion is one of the important factors due to some characteristics such as operating environment, service life and transportation medium, etc., which damages the integrity of the pipeline and damage the normal operation of pipelines. Furthermore, leakage accidents caused by pipeline corrosion have occurred all over the world, accounting for 70~90% of total accidents, which has caused huge economy losses and catastrophic environmental damage.