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Design and Application of a Fault Detection Method Based on Adaptive Filters and Rotational Speed Estimation for an Electro-Hydrostatic Actuator
Published on: October 28, 2022
Real-time defect detection of steel wire rods using wavelet filters optimized by univariate dynamic encoding
Jong Pil Yun1, Yong-Ju Jeon, Doo-chul Choi
1System Research Group, Engineering Research Center, Pohang Iron and Steel Company (POSCO), Pohang 790-300, South Korea.
Summary
A novel defect detection algorithm effectively identifies flaws on scale-covered steel wire rods using adaptive wavelet filters and an enhanced double-threshold method for improved accuracy in industrial settings.
Area of Science:
- Materials Science
- Signal Processing
- Computer Vision
Background:
- Surface defects on steel wire rods can compromise product quality and require reliable detection methods.
- Traditional defect detection techniques may struggle with complex surface textures like scale coverage.
Purpose of the Study:
- To develop and evaluate a new algorithm for detecting defects on scale-covered steel wire rods.
- To enhance the accuracy and flexibility of wavelet-based defect detection.
Main Methods:
- An adaptive wavelet filter designed using lattice parameterization of orthogonal wavelet bases.
- Undecimated discrete wavelet transform with separate column and row filter design.
- Optimization of wavelet filter coefficients using the univariate dynamic encoding algorithm for searches (uDEAS).
- An enhanced double-threshold method for improved detection accuracy.
Main Results:
- The proposed algorithm demonstrated effectiveness in detecting defects on steel wire rod surface images.
- The adaptive wavelet filter design and enhanced thresholding improved detection performance.
Conclusions:
- The developed algorithm offers a robust solution for defect detection in industrial steel production.
- The integration of adaptive wavelets and advanced thresholding enhances defect identification accuracy.
