Oct-2024

Corrosion monitoring techniques and benefits of newer methods

By leveraging the power of AIoT, industry can overcome the hidden challenges of permanent ultrasonic sensor solutions and achieve superior operational performance.

Venkat Eswara and Sascha Schieke
mPACT2WO, a Molex Business

Article Summary

The oil and gas production industry is facing substantial financial challenges attributed to corrosion, incurring annual costs estimated at $1.372 billion.1 These costs, along with environmental risks, are expected to rise as operations move into more demanding environments. Corrosion poses a major threat in process industries, especially in asset-intensive sectors like oil and gas and petrochemicals. It can cause leaks, reduce asset performance, and lead to unplanned shutdowns and potentially catastrophic incidents. Additionally, corrosion introduces operational risks, safety concerns, and liabilities. As a result, corrosion monitoring has become a critical aspect of plant operations and maintenance, with detection methods and procedures continuously evolving.

Evolution of corrosion monitoring techniques
Traditionally, ultrasonic thickness monitoring has been performed manually using portable ultrasonic equipment. However, manual inspections are time-consuming and costly, and they require extensive data cleaning to address the method’s limitations. To mitigate these shortcomings, permanently installed ultrasonic solutions were introduced. Table 1 highlights the main differences between manual inspections and those using permanently installed automated sensors.²

Permanently installed ultrasonic solutions have been in use for more than three decades across various industries, but they present challenges regarding the accuracy and reliability of thickness measurements. These challenges can be addressed by adopting Artificial Intelligence of Things (AIoT) technology. By leveraging AIoT, industries can significantly enhance the accuracy, precision, and reliability of ultrasonic corrosion monitoring, thereby improving asset integrity and operational efficiency.

Ultrasonic measurement: accuracy and precision
The quality of ultrasonic thickness data is determined by its accuracy, which refers to how close the measurements are to the true thickness, and its precision, which refers to how consistent the measurements are around an average thickness value. This relationship is illustrated in Figure 1.

However, the accuracy of ultrasonic thickness measurements may not necessarily improve with permanently installed sensors. This is because the accuracy depends on a different set of factors. This becomes evident when examining the principle of ultrasonic thickness measurements and the essential equation used to calculate thickness.

Both portable and permanently installed ultrasonic instruments measure the time-of-flight (ToF) of ultrasonic waves in a part.

The precision of ultrasonic thickness measurements is enhanced by using permanently installed ultrasonic sensors, as they eliminate factors contributing to the variability of manual measurements. These factors include the exact location of the probe, probe handling, differences in temperature compensation, and standardisation.

The thickness t is determined using Equation 1 by multiplying the ToF data by the temperature-dependent material velocity v (T) and dividing by two:
                                                      (1)

Industry standards and literature offer the necessary correction factors for various materials across different temperature ranges. Both portable and permanently installed instruments exhibit high accuracy in terms of ToF measurements and signal processing, with up-sampling further enhancing precision. However, the material velocity is typically estimated, with standard values provided in industry guidelines.

Since material velocity is temperature-dependent, additional corrections are needed to determine thickness at room temperature when measurements are taken at temperatures above or below room temperature. Industry standards and literature offer necessary correction factors for various materials across different temperature ranges. An important note is that, like material velocity, these temperature corrections are approximate, as they do not account for variations in material properties due to the production process or changes caused by environmental conditions over the part’s service life.

Corrosion impact on ultrasonic accuracy
Permanently installed solutions that rely solely on traditional methods for material velocity and temperature correction do not inherently enhance the accuracy of thickness measurements. Furthermore, another source of variation arises from the nature of ultrasonic waves and their interaction with irregular surfaces. Corrosion and erosion not only lead to metal loss but also cause the affected surfaces to become more irregular and rougher. As a result, ultrasonic echoes reflected from a corroding surface are usually smaller in amplitude and broader compared to echoes from clean surfaces.

In extreme cases, these irregular surfaces can create double or triple peaks in an echo, as illustrated in Figure 2. Echoes from a relatively smooth surface are usually narrow with a relatively high amplitude (top panel). In contrast, echoes reflected from a corroding surface typically have a smaller amplitude and are broader (bottom panel). In some cases, the echo splits into two or more peaks.³

Echo deterioration and ToF variability
The challenge with echo deterioration due to corroding surfaces is the accurate identification of ToF reference points, as depicted by the red dots in Figure 2. As ultrasonic echoes deteriorate, these reference points shift, causing uncorrelated variations in their positions. Consequently, the ToF values change over time, leading to alterations in the thickness trends according to Equation 1. These variations often manifest as distinct multiples of the half-wave distance, resulting in step-function-like changes in the thickness trendline.

In some instances, these changes produce a steady increase in ToF, which can be misinterpreted as an accumulation of deposits on the internal surface of the part being examined, as shown in Figure 3. Such misinterpretations can lead to critical situations that impact the safe operation of the plant.

The blue line in Figure 3 illustrates the apparent thickness trend, which is unaffected by temperature changes and results from shifting ToF reference points caused by active corrosion.⁴ In contrast, the orange line represents the actual corrosion trend, determined using machine learning techniques to account for variations in the ultrasonic waveform.³