How do you interpret and analyze the data from acoustic emission and ultrasonic testing?

1 Acoustic emission testing

Acoustic emission testing (AET) is a technique that monitors the sound waves produced by a material or structure when it is subjected to stress, such as loading, unloading, or temperature changes. The sound waves are captured by sensors attached to the surface of the material or structure and converted into electrical signals. The signals are then analyzed by software to determine the location, intensity, frequency, and type of the acoustic emission sources, which indicate the presence and severity of defects, cracks, or other damage.

2 Ultrasonic testing

Ultrasonic testing (UT) is a technique that uses high-frequency sound waves to inspect the internal structure of a material or structure. The sound waves are generated by a transducer that sends and receives pulses of ultrasound. The sound waves travel through the material or structure and reflect or refract at the boundaries of different phases, such as air, liquid, or solid. The reflected or refracted waves are detected by the transducer and displayed on a screen or recorded by software. The time, amplitude, and shape of the waves can reveal the thickness, shape, size, and location of defects, flaws, or other features.

3 Data acquisition and processing

Both AET and UT require proper data acquisition and processing to ensure accurate and reliable results. Data acquisition involves selecting and calibrating the appropriate sensors, transducers, amplifiers, filters, cables, and other equipment for the test. It also involves setting up the test parameters, such as frequency, gain, range, duration, and trigger. Data processing involves applying algorithms, filters, noise reduction, signal enhancement, and other techniques to improve the quality and clarity of the data. It also involves converting the data into a format that can be stored, transmitted, or displayed.

4 Data analysis and interpretation

Data analysis and interpretation involve the use of software tools and standards to extract meaningful information from the data and evaluate the condition and performance of the material or structure. For AET, these tools include AE source location, which uses triangulation or other methods to determine coordinates of AE sources; AE source characterization, which uses pattern recognition or other methods to classify AE sources; AE source quantification, which uses statistical or other methods to measure intensity, frequency, energy, count, or duration of AE sources; and AE source evaluation, which uses criteria or standards to assess severity, significance, and risk of AE sources. For UT, these tools include UT image reconstruction, which uses tomography or other methods to reconstruct a two-dimensional or three-dimensional image; UT flaw detection, which uses thresholding or other methods to identify flaws; UT flaw characterization, which uses sizing or other methods to estimate shape, size, orientation, and location of flaws; and UT flaw evaluation, which uses criteria or standards to assess severity, significance, and risk of flaws.

5 Data reporting and documentation

Data reporting and documentation involve presenting and communicating the results of the data analysis and interpretation to the relevant stakeholders, such as clients, managers, engineers, or regulators. This should include a summary of the test objectives, methods, procedures, equipment, parameters, and standards used; a description of the material or structure tested; a presentation of the data acquired and processed; an explanation of the data analysis and interpretation methods; a discussion of the findings, conclusions, recommendations, limitations, uncertainties, and implications of the data analysis and interpretation; and a list of references, sources, appendices, or attachments that support the data reporting and documentation.

6 Data quality and integrity

Data quality and integrity are essential for ensuring the validity and reliability of the data analysis and interpretation. This depends on various factors, such as the accuracy, precision, sensitivity, resolution, and repeatability of the data acquisition and processing methods and equipment; the completeness, consistency, correctness, and clarity of the data acquired and processed; the objectivity, credibility, transparency, and traceability of the data analysis and interpretation methods, tools, criteria, and assumptions; as well as the verification, validation, calibration, testing, and auditing of the data acquisition, processing, analysis, and interpretation processes and results. Additionally, security measures should be taken to ensure the confidentiality of the data reported and documented.

7 Here’s what else to consider

This is a space to share examples, stories, or insights that don’t fit into any of the previous sections. What else would you like to add?