A Study of Phased-Array Ultrasonic Testing (PAUT) for Detecting, Sizing, and Characterizing Flaws in the Welds of Existing Hydraulic Steel Structures (HSS)
Abstract Hydraulic steel structures (HSS) are components of navigation, flood control, and hydropower projects that control or regulate the flow of water.
Damage accumulates in HSS as they are operated over time, and they must be inspected periodically. This is often accomplished using nondestructive testing (NDT) techniques.
If damage is detected, the structure’s fitness for continued service must be evaluated, which requires information on the location and size of discontinuities.
This information can be obtained using ultrasonic testing (UT) techniques.
However, there is limited information on the reliability of UT techniques with respect to detecting, sizing, and characterizing flaws in HSS.
This study addresses this gap.
Round-robin experiments were carried out using phased-array ultrasonic testing (PAUT) to scan weld specimens representing a variety of HSS geometries.
The results of the round-robin experiments were analyzed to estimate the probability of detection (POD) and to assess the influence of factors potentially affecting POD.
Uncertainty in estimates of flaw length and height were described, and partial safety factors were derived for use in fitness-for-service analyses.
These results demonstrate the importance of the technician as a factor influencing the reliability of NDT techniques applied to HSS.
Condition Monitoring (CM): the process of monitoring specific parameters (like vibration, temperature, lubricant condition, etc.) of critical equipment or systems to detect any significant change which might indicate a developing fault.
It’s a “snapshot” of the current condition at a particular time.
Predictive Maintenance (PdM): involves using data-driven, pro-active maintenance methodologies to predict when equipment failure might occur, so maintenance can be performed just in time to avoid unplanned downtime.
Predictive Maintenance often uses the data obtained from Condition Monitoring, but appliesanalytics, algorithms, and sometimes machine learning to predict future failures.
Maintenance vs. Monitoring
Purpose: CM: Detect and monitor changes in machine conditions to highlight potential problems.
PdM: Forecast when a machine will fail or when a maintenance task should be performed to prevent an unplanned outage.
Method: CM: Regular or continuous measurement of equipment parameters and comparison against predefined standards or baselines.
PdM: Analysis of data trends and patterns (often with the help of advanced software tools) to predict the future condition of the equipment and schedule maintenance accordingly.
Frequency: CM: Monitoring can be continuous, daily, weekly, monthly, etc., depending on the criticality of the equipment.
PdM: The frequency is determined by data trends and the analytics’ outcomes, leading to predictions on when maintenance might be required next.
Digital Radiography: The use of digital imaging techniques for radiographic testing, which offers improved sensitivity and resolution compared to traditional film-based methods.
Eddy Current Testing (ECT): ECT is widely used for surface and near-surface defect detection, and advancements in probe design and signal processing have increased its applicability.
Phased Array Ultrasonics: This is a specialized ultrasonic testing technique that employs multiple small ultrasonic elements to steer and focus sound waves, allowing for rapid scanning and defect characterization.
Computed Tomography (CT) Scanning: CT scanning is a powerful technique that provides 3D images of internal structures, making it valuable for inspecting complex components and composite materials.
Infrared Thermography: This technique uses thermal imaging to detect variations in temperature, which can indicate defects or anomalies in materials.
Acoustic Emission Testing (AE): AE is used for continuous monitoring and detection of active defects, crack growth, or structural integrity of components under stress.
Guided Wave Testing (GWT): GWT is used for long-range inspection of pipes, plates, and other structures, allowing for the assessment of large areas from a single probe position.
Electromagnetic Testing (ET): ET includes methods like Magnetic Particle Testing and Eddy Current Testing, which are used for surface and subsurface defect detection.
NDT in Additive Manufacturing: With the growing use of additive manufacturing (3D printing) in various industries, there is an increasing demand for NDT techniques to ensure the quality of printed components.
Stainless steel and aluminum are popular materials for NDT inspection plugs due to their specific properties that make them suitable for harsh environments.
Stainless Steel Plugs
Durability: Stainless steel is known for its high strength and durability, which makes it ideal for use in environments that experience high stress or mechanical wear.
Corrosion Resistance: It offers excellent resistance to corrosion, particularly from chemicals and moisture, making it suitable for applications in chemical plants, marine environments, and other areas where corrosion is a concern.
Temperature Resistance: Stainless steel can withstand extreme temperatures, both high and low, without losing its structural integrity, which is essential for various industrial applications.
Aluminum Plugs
Lightweight: Aluminum is significantly lighter than stainless steel, which makes it easier to handle and install, especially in applications where weight is a critical factor.
Corrosion Resistance: While not as corrosion-resistant as stainless steel, aluminum still offers good resistance to corrosion, particularly when it is anodized or coated, which makes it suitable for many industrial environments.
Conductivity: Aluminum has good thermal and electrical conductivity, which can be beneficial in certain NDT applications where these properties are required.
