This paper presents a technique that can be used to fuse data from multiple sensors that are employed in nondestructive evaluation (NDE) applications, specifically for the in-line inspection of gas transmission pipelines. A radial basis function artificial neural network is used to perform geometric transformations on data obtained from multiple sources. The technique allows the user to define the redundant and complementary information present in the data sets. The efficacy of the algorithm is demonstrated using experimental images obtained from the NDE of a test specimen suite using magnetic flux leakage (MFL), ultrasonic (UT) and thermal imaging methods. The results presented in this paper indicate that neural network based geometric transformation algorithms show considerable promise in multi-sensor data fusion applications.
|Original language||English (US)|
|Title of host publication||2008 IEEE International Instrumentation and Measurement Technology Conference Proceedings, I2MTC|
|Number of pages||4|
|State||Published - 2008|
|Event||2008 IEEE International Instrumentation and Measurement Technology Conference, I2MTC - Victoria, BC, Canada|
Duration: May 12 2008 → May 15 2008
|Name||Conference Record - IEEE Instrumentation and Measurement Technology Conference|
|Other||2008 IEEE International Instrumentation and Measurement Technology Conference, I2MTC|
|Period||5/12/08 → 5/15/08|
All Science Journal Classification (ASJC) codes
- Electrical and Electronic Engineering
FingerprintDive into the research topics of 'Multi-sensor data fusion using geometric transformations for gas transmission pipeline inspection'. Together they form a unique fingerprint.
Virtual Reality Lab
Shreekanth Mandayam (Manager) & George D. Lecakes (Manager)