Name
Title | ||
|---|---|---|
Window Feature-Based Two-Stage Defect Identification Using Magnetic Flux Leakage Measurements. |
Abstract | ||
|---|---|---|
Magnetic flux leakage (MFL) testing, one of the nondestructive testing methods, is widely adapted by approximately 90% of in-service pipelines. It is very important to identify defects in MFL testing. This paper presents a method to detect defects and determine precise defect regions using window features generated from MFL measurements. The main contributions are: four novel window features of de... |
Year | DOI | Venue |
|---|---|---|
2018 | 10.1109/TIM.2017.2755918 | IEEE Transactions on Instrumentation and Measurement |
Keywords | Field | DocType |
Testing,Feature extraction,Pipelines,Hidden Markov models,Inspection,Magnetic circuits,Bayes methods | Magnetic flux leakage,Pattern recognition,Salience (neuroscience),Nondestructive testing,Feature extraction,Electronic engineering,Fingerprint,Artificial intelligence,Hidden Markov model,Mathematics,Magnetic circuit,Bayesian probability | Journal |
Volume | Issue | ISSN |
67 | 1 | 0018-9456 |
Citations | PageRank | References |
1 | 0.39 | 7 |
Authors | ||
5 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Jinhai Liu | 1 | 92 | 10.16 |
| Mingrui Fu | 2 | 4 | 1.11 |
| Feilong Liu | 3 | 429 | 15.52 |
| Jian Feng | 4 | 90 | 19.86 |
| Kuangqing Cui | 5 | 1 | 0.39 |