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Related Experiment Video

Updated: Dec 18, 2025

Quantifying the Relative Thickness of Conductive Ferromagnetic Materials Using Detector Coil-Based Pulsed Eddy Current Sensors

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Published on: January 16, 2020

Defect Classification Using Postpeak Value for Pulsed Eddy-Current Technique.

Jiuhao Ge^1,2, Chenkai Yang¹, Ping Wang¹

¹Nondestructive Detection and Monitoring Technology for High Speed Transportation Facilities, Key Laboratory of Ministry of Industry and Information Technology, Nanjing University of Aeronautics and Astronautics, Nanjing 210016, China.

Sensors (Basel, Switzerland)

|June 21, 2020

Summary

A new postpeak value feature improves pulsed eddy current technique (PECT) for defect detection. This method effectively classifies surface and reverse defects in real-time without needing supervised training.

Keywords:

TMR sensor absolute signal nondestructive testing time domain

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Area of Science:

Electromagnetic testing
Non-destructive evaluation
Materials science

Background:

The pulsed eddy current technique (PECT) is widely used for non-destructive testing.
Distinguishing between surface and reverse defects using traditional PECT features can be challenging.
Developing advanced signal processing methods is crucial for enhancing PECT's diagnostic capabilities.

Discussion:

This study introduces a novel 'postpeak value' feature for PECT analysis.
The proposed feature is evaluated for its effectiveness in classifying surface versus reverse defects.
A PECT system was developed and utilized for experimental verification of the proposed method.

Key Insights:

The postpeak value demonstrates superior performance over the traditional peak value for detecting reverse defects.
Conversely, the traditional peak value shows better performance for identifying surface defects.
The developed classification algorithm enables real-time defect classification with an easily understandable process and requires no supervised training.

Outlook:

Further research could explore optimizing the postpeak feature for different material types and defect geometries.
Integration of this classification algorithm into automated inspection systems could enhance industrial applications.
Investigating hybrid features combining peak and postpeak values may offer even greater classification accuracy.