Optimization of Neural Network Parameters for Defect Characterization

Abstract

Natural gas, which is one of the nation’s cheapest forms of energy, is transported to consumer sites via a vast transmission pipeline network .Safety considerations and a desire to assure uninterrupted energy supply require that the pipelines be inspected periodically. The effective detection of defects in the pipeline is vital to assure the integrity of the transmission systems. A variety of nondestructive evaluation techniques (NDE), such as ultrasonic, eddy current, and magnetic flux leakage (MFL) methods have been employed to detect defects in gas pipelines [1]. Among these methods, the MFL method represents the commonly used technique.

Original document

The different versions of the original document can be found in:

• https://lib.dr.iastate.edu/cgi/viewcontent.cgi?article=3331&context=qnde

• http://www.springerlink.com/index/pdf/10.1007/978-1-4613-0383-1_287,

http://dx.doi.org/10.1007/978-1-4613-0383-1_287

• https://lib.dr.iastate.edu/qnde/1996/allcontent/287,

https://link.springer.com/chapter/10.1007/978-1-4613-0383-1_287,

https://core.ac.uk/display/38894674,

https://lib.dr.iastate.edu/cgi/viewcontent.cgi?article=3331&context=qnde,

https://academic.microsoft.com/#/detail/188166680