Location

Seattle, WA

Start Date

1-1-1996 12:00 AM

Description

Defect related information present in NDE signals is frequently obscured by the presence of operational variables inherent in the system. A typical NDE system comprises of an energy source, a test specimen and a sensor array. Operational variables include uncontrollable changes in source signal strength and/or frequency, variations in the sensitivity of the sensor and alterations in the material properties of the test specimen. These operational variables can confuse subsequent signal interpretation schemes, such as those relying on artificial neural networks. Invariant pattern recognition methods are required to ensure accurate signal characterization in terms of the underlying defect geometry. This paper describes a generalized invariance transformation technique to compensate for operational variables in NDE systems. An application to magnetic flux leakage (MFL) inspection of gas transmission pipelines is presented. The technique is employed to compensate for variations in magnetization characteristics in the pipe wall.

Volume

15A

Chapter

Chapter 3: Signal Processing and Image Analysis

Section

Signal Processing

Pages

805-812

DOI

10.1007/978-1-4613-0383-1_105

Language

en

File Format

application/pdf

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Jan 1st, 12:00 AM

Fuzzy Inference Systems for Invariant Pattern Recognition in MFL NDE

Seattle, WA

Defect related information present in NDE signals is frequently obscured by the presence of operational variables inherent in the system. A typical NDE system comprises of an energy source, a test specimen and a sensor array. Operational variables include uncontrollable changes in source signal strength and/or frequency, variations in the sensitivity of the sensor and alterations in the material properties of the test specimen. These operational variables can confuse subsequent signal interpretation schemes, such as those relying on artificial neural networks. Invariant pattern recognition methods are required to ensure accurate signal characterization in terms of the underlying defect geometry. This paper describes a generalized invariance transformation technique to compensate for operational variables in NDE systems. An application to magnetic flux leakage (MFL) inspection of gas transmission pipelines is presented. The technique is employed to compensate for variations in magnetization characteristics in the pipe wall.