DEFECT CLASSIFICATION USING A NEW FEATURE FOR PULSED EDDY CURRENT SENSORS

By Guiyun Tian, Ali Sophian. 2005

215 citations recorded by the source corpus.

Subfields: Mechanical Engineering, Mechanics of Materials

View Paper: https://www.semanticscholar.org/paper/368c7f09dddc3155c02479939e45b07ca58f8c0e

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Mechanical Engineering

1. Yunze He
2. Guiyun Tian
3. Hong Zhang
4. Yong Li
5. Catalin Mandache

In Mechanics of Materials

1. Yunze He
2. Guiyun Tian
3. Hong Zhang
4. Yong Li
5. Catalin Mandache

Citations in the corpus

• Steel Corrosion Characterization Using Pulsed Eddy Current Systems (2012, methodology): “Various time-domain features, namely time to peak, peak height and the more recently reported, rising point and zero crossing, extracted from the PEC response are used to identify and characterise defects in metallic targets [34], [35], [42].”
• Using Deep Learning to Detect Defects in Manufacturing: A Comprehensive Survey and Current Challenges (2020, background): “Factors that influence the accuracy of the detection of eddy current are the type and parameters of coil and material and the profile of the test piece [43,44].”
• Study of Lift-Off Invariance for Pulsed Eddy-Current Signals (2009, background): “However, to the authors’ knowledge, quantitative analysis of PEC signals for conductor evaluation and defect characterization [3], [5], [6] has not been fully investigated.”