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Ultrasonography

Ultrasonography

Ultrasonography is an imaging technique that uses high-frequency sound waves to visualize the body's internal structures. It is a non-invasive and safe procedure that does not involve the use of ionizing radiation, making it widely used in various medical fields. Ultrasonography is used to study heart function, blood flow in the neck or extremities, certain conditions such as gallbladder disease, and fetal growth and development.
During an ultrasonography procedure, a handheld device called a...

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Experiments on Ultrasonic Lubrication Using a Piezoelectrically-assisted Tribometer and Optical Profilometer

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Study on Preprocessing Methods for Ultrasonic Signals from Internal Defects in Rolls.

Baotong Chen¹, Xiaolong Hu², Xuguo Yan^2,3

¹Key Laboratory of Metallurgical Equipment and Control Technology, Wuhan University of Science and Technology, Ministry of Education, Wuhan 430081, China.

Sensors (Basel, Switzerland)

|June 26, 2026

Summary

This summary is machine-generated.

This study introduces a new ultrasonic signal preprocessing technique to improve internal roll defect detection. The method significantly enhances defect identification accuracy by reducing background clutter, outperforming traditional filtering approaches.

Keywords:

feature extraction internal defect roll ultrasonic testing

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Area of Science:

Non-destructive testing
Materials science
Signal processing

Background:

Ultrasonic testing is vital for industrial safety and quality control.
Background clutter in ultrasonic signals complicates defect detection, especially for near-surface flaws.
Weak defect echoes can be obscured by surface echoes, hindering accurate analysis.

Purpose of the Study:

To develop a novel ultrasonic signal preprocessing method for enhanced internal roll defect detection.
To improve the accuracy of subsequent defect identification models by suppressing background clutter.
To overcome limitations of existing methods in handling complex ultrasonic signals.

Main Methods:

Acquisition of ultrasonic signals from defective and defect-free regions.
Application of an improved median filter to remove noise and outliers.
Construction of a multi-stage Finite Impulse Response (FIR) filter optimized with particle swarm optimization for clutter estimation.
Signal subtraction to obtain clutter-suppressed defect signals.
Validation using a Convolutional Neural Network (CNN) classifier on a dataset of 5000 samples.

Main Results:

The proposed method significantly reduces background clutter in ultrasonic signals.
Defect identification accuracy improved by approximately 38% over median filtering and 20% over wavelet denoising.
High recall rates were achieved, demonstrating robust defect detection capabilities.
The preprocessed signals led to superior performance in CNN-based defect classification.

Conclusions:

The novel ultrasonic signal preprocessing method effectively suppresses background clutter.
The technique substantially enhances the accuracy and reliability of internal roll defect detection.
This approach offers a significant advancement over traditional denoising methods for ultrasonic testing.