Fuzzy Inference Systems for Invariant Pattern Recognition in MFL NDE
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.
KeywordsMembership Function Radial Basis Function Fuzzy Inference System Fuzzy Rule Base Gaussian Membership Function
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