Improve Depth Metrics Related to Tool Tolerance and NDE

Decrease depth uncertainty across the entire pipeline system

Utilizes standard machine learning methodologies to statistically highlight where “depth uncertainty” may exist as a result of potential issues with tool tolerance, NDE, and all other measurement variables. We are looking at all inline inspection (ILI) feature data and registering it in bulk.

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Pinpoint negative growth by analyzing every piece of feature data that has been gathered over the years

Correlate organizational operating thresholds as part of the trained data used to teach the algorithm

Increase the confidence levels by leveraging additional data such as NDE, coating, soil, etc., to allow higher algorithm probabilities

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Learn how Cognitive Integrity Management can prevent pipeline failures - pay as you go.

We have helped Fortune 500 companies like you with predicting pipeline failures with the assistance of machine learning. Let us show you how.

Make pipeline failures a thing of the past.

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