Invariance algorithms for processing NDE signals

Shreekanth Mandayam, Lalita Udpa, Satish S. Udpa, William Lord

Research output: Contribution to journalConference articlepeer-review


Signals that are obtained in a variety of nondestructive evaluation (NDE) processes capture information not only about the characteristics of the flaw, but also reflect variations in the specimen's material properties. Such signal changes may be viewed as anomalies that could obscure defect related information. An example of this situation occurs during in-line inspection of gas transmission pipelines. The magnetic flux leakage (MFL) method is used to conduct noninvasive measurements of the integrity of the pipe-wall. The MFL signals contain information both about the permeability of the pipe-wall and the dimensions of the flaw. Similar operational effects can be found in other NDE processes. This paper presents algorithms to render NDE signals invariant to selected test parameters, while retaining defect related information. Wavelet transform based neural network techniques are employed to develop the invariance algorithms. The invariance transformation is shown to be a necessary pre-processing step for subsequent defect characterization and visualization schemes. Results demonstrating the successful application of the method are presented.

Original languageEnglish (US)
Pages (from-to)210-216
Number of pages7
JournalProceedings of SPIE - The International Society for Optical Engineering
StatePublished - Dec 1 1996
Externally publishedYes
EventNondestructive Evaluation of Materials and Composites - Scottsdale, AZ, United States
Duration: Dec 3 1996Dec 3 1996

All Science Journal Classification (ASJC) codes

  • Electronic, Optical and Magnetic Materials
  • Condensed Matter Physics
  • Computer Science Applications
  • Applied Mathematics
  • Electrical and Electronic Engineering


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