Show simple item record

dc.contributor.authorGrossman, Barry G.
dc.contributor.authorGonzales, Frank S.
dc.contributor.authorBlatt, Joel H.
dc.contributor.authorCahall, Scott C.
dc.date.accessioned2017-10-06T16:50:00Z
dc.date.available2017-10-06T16:50:00Z
dc.date.issued1993-05-28
dc.identifier.citationrossman, B. G., Gonzalez, F. S., Blatt, J. H., & Cahall, S. C. (1993). Detection and location of pipe damage by artificial-neural-net-processed moire error maps. Paper presented at the Proceedings of SPIE - the International Society for Optical Engineering, , 1821 415-427.en_US
dc.identifier.urihttp://hdl.handle.net/11141/1788
dc.descriptionAlgorithms, Artificial intelligence, Failure analysis, Moire fringes, Pipeen_US
dc.description.abstractA novel automated inspection technique to recognize, locate, and quantify damage is developed. This technique is based on two already existing technologies: video moire metrology and artificial neural networks. Contour maps generated by video moire techniques provide an accurate description of surface structure that can then be automated by means of neutral networks. Artificial neural networks offer an attractive solution to the automated interpretation problem because they can generalize from the learned samples and provide an intelligent response for similar patterns having missing or noisy data. Two dimensional video moire images of pipes with dents of different depths, at several rotations, were used to train a multilayer feedforward neural network by the backpropagation algorithm. The backpropagation network is trained to recognize and classify the video moire images according to the dent's depth. Once trained, the network outputs give an indication of the probability that a dent has been found, a depth estimate, and the axial location of the center of the dent. This inspection technique has been demonstrated to be a powerful tool for the automatic location and quantification of structural damage, as illustrated using dented pipes.en_US
dc.language.isoen_USen_US
dc.rightsThis published article is made available in accordance with publishers policy. It may be subject to U.S. copyright law.en_US
dc.rights.urihttp://spie.org/publications/journals/guidelines-for-authors#Terms_of_Useen_US
dc.titleDetection and location of pipe damage by artificial-neural-net-processed moire error mapsen_US
dc.typeConference Proceedingen_US
dc.identifier.doi10.1117/12.145558


Files in this item

Thumbnail

This item appears in the following Collection(s)

Show simple item record