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The intention of this work is to pose epistemic questions about corrosion measurement, statistical inference, and the role of machine learning in predicting corrosion growth. The audience of this work is practitioners implementing inferential algorithms or tools for corrosion prediction. In this work, an algorithm consists of a process for estimating the presence and severity of corrosion.
Machine learning as a tool for automation has grown exponentially in the past two decades. Growth has come from innovations in hardware, such as powerful graphics processing units (GPUs) and cloud computing. Along with hardware advances, there has been an explosion in software packages, algorithms, and tools for performing machine learning. This innovation has resulted in a landscape full of vendor offerings with bold claims about accuracy and precision while not offering much subject matter expertise. The corrosion industry is no exception and faces unique risks in evaluating machine learning tools. Unlike many consumer-grade tools, the cost of corrosion detection and estimation errors are unbalanced; the cost of a false negative (the algorithm reports no corrosion; however, corrosion is present) is higher than in consumer applications. In addition, corrosion has a number of mechanisms and morphologies, so operators must be cautious and understand the limitations of using any one-size-fits-all machine learning tool. For any algorithm, the output is only as good as the data used to train it, so operators must be aware of possible sources of error and bias in training datasets. This work considers the above constraints and offers a checklist for algorithm developers to consider when assessing corrosion with machine learning.
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.
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In a pipe, guided Lamb-like waves can propagate around the circumference of the pipe wall. As they do, the waves pick up details about the pipe wall’s characteristics, such as its inner surface condition and, most significantly, its thickness. A robust pipe wall thickness estimation method based on conventional (i.e., non-machine learning) processing methods has been proposed by the authors.
Managing external corrosion, especially for underground assets, is a significant challenge dating back to the first underground pipeline in 1865. The very first issue of the journal, CORROSION, featured a headline story on this subject. This subject is fundamental for corrosion engineers and pipeline operators.