The benefit of combining technologies for the inspection of pipelines lies primarily in the combination of data sets when analyzing features. With more information, and different perspectives, the personal interpretation that is often needed with identifying features becomes less necessary as the data becomes clearer. This unique method of combined technologies allows for more accurate and reliable integrity assessments.

Industrial assets are exposed to a wide range of potential damage mechanisms

In pipelines, for example, while single threats may compromise operational safety, combined threats such as corrosion within dents or cracking in corrosion pose an even higher risk to the asset’s overall integrity. To guarantee the most accurate picture of a pipeline’s integrity, one technology is sometimes not sufficient. Combined Diagnostics Solutions combines the individually strong technologies to produce multiple data sets that can deliver the full picture of an asset.

The classic proven method to overcome non-negligible risks of one control system in critical cases is using two, which is called the “four-eyes principle.” The mathematical calculation of the Probability of Detection makes this plausible and seemingly straightforward:

Having two systems, a and b, with independent Probability of Detection, P, results in a “combined” value:

Using an ILI system with a detection probability of 92% may not be tolerable, but combining it with another with a POD as low as 88% provides more than 99% performance.

More than the math

In reality, ILI data analysis benefits even more from independent measurements than mathematical formulas suggest, because a significant component in understanding the information is data interpretation based on gained experience. Even neural networks do the same. Machine learning systems have similar procedures: they collect various data sets from different sources and interpret the entire picture to get the ideal result. Oftentimes, even non-ideal data sets can lead to unexpected success.

One theoretical example for two such data sets would be a standalone Ultrasonic (UT) measurement looking like a missing signal that may not be interpreted as an anomaly. Equally, a small-scale spot in standalone Magnetic Flux Leakage (MFL) data may be interpreted as a pinhole of 4 mm in diameter at 40% depth. Combining these two observations at the same location will result in the identification of a 2-mm pinhole with more than 80% depth. This change is made possible because the MFL signal was reinterpreted based on the very small extent visible in UT. This demonstrates that combined interpretative differentiation is more than number crunching. Averaging results is not sufficient; in this case, 80% maximum depth was identified instead of the original 40%.

Two or more measurements of the same asset dramatically improve the detection and classification of features. The combination overcomes single-system methodological restrictions and improves interpretative sizing. Probability of detection, Identification and sizing accuracy are significantly increased when complimentary ILI systems are combined. This leads to more accurate and reliable integrity assessments, reducing both operational risk and field verification cost.