Automatic Classification and Characterization of Hidden Corrosion Using Pulsed Eddy Current Data

Detection and characterization of the material loss due to corrosion in aircraft fuselage joints plays an important role in life management of aging aircraft. Pulsed eddy current has been shown to effectively characterize hidden corrosion in lap splices. However, variation of the probe lift-off and interlayer gap can cause false indications or inaccuracy in quantification. This paper presents the results of a project concerned with the development of a technique for automatic material loss characterization based on pulsed eddy current data. The goal of the project was to develop a software tool that would facilitate distinguishing of material loss in multi-layer structures from effects such as probe lift-off and interlayer gap. Following on from previous work addressing the capabilities of time-frequency analysis of pulsed eddy current signals to provide specific visual patterns that can be related to the interlayer gap, lift-off, and material loss, the authors have investigated this method along with an intelligent system to automatically characterize and determine the location of material loss in a two-layer structure. Introduction: The lap joint is a common structural element in transport aircraft. These joints are potentially susceptible to corrosion and fatigue damage, and require non-destructive testing to ensure safety over the life of an aircraft. Nondestructive detection and quantification of corrosion in these multilayered structures is a challenge in which the pulsed eddy current method has shown potential [1,2]. Pulsed eddy current techniques excite the probe’s driving coil with a repetitive broadband pulse, usually a square wave. This changing current through the coil induces eddy currents in the test piece and the associated magnetic field in the material dissipates rapidly and exponentially as it approaches a steady state. The field at the surface of the test specimen is measured with a sensor, which may be the driving coil, another coil, or a Hall or GMR sensor. The resulting pulsed eddy current signals are a series of voltage-time data. Since a broad frequency spectrum is produced, unlike the conventional eddy current testing, the reflected signal contains depth information about the material. Analysis of pulsed eddy current signals in time domain provides some features such as amplitude, time-to-peak or time-to zero-crossing that have been used for detection and characterization of material loss due to corrosion in multi-layer structures. Two of the most important noise sources in lap joint inspections are the variation in probe lift-off and interlayer gap due to change in paint or adhesive thickness, and corrosion pillowing. Previous work of the authors [3] has shown that the time-frequency analysis of pulsed eddy current signals provides visual discrimination between the simultaneous occurrence of material loss and changes in interlayer gap or lift-off. However, this method cannot currently be readily used because of difficulties in calibration and the lack of an automatic detection and classification system. This paper presents a method for automatic analysis of pulsed eddy current signal capable of detecting and classifying the material loss due to corrosion, and determining its location in a twolayer structure. Principal of method: Conventional pulsed eddy current techniques for corrosion detection and characterization rely on the analysis of signal features that are represented as c-scan images. Only experienced operators are able to perform full evaluation of pulsed eddy current c-scan images. Increasing emphasis on reliability and demand for tools that can assist operators have motivated research for an intelligent pulsed eddy current detection and classification system. The automatic pulsed eddy current detection and classification system developed in this work includes three modules: time-frequency analysis module, feature extraction module and classification module, as shown in Figure 1. Time-Frequency Analysis Feature Extraction Classification PEC signal Time-Frequency Image Feature Set Defect Class Figure 1: Modular architecture for the proposed pulsed eddy current system Details about each module are described in the following. Time-Frequency Module: Time-frequency analysis provides a three-dimensional representation of signals in time-frequency-amplitude space, but usually, the projection of this three-dimensional representation is shown in the two-dimensional time-frequency plane with grey scale representing the amplitude. There are several possible time-frequency distributions; however, we will focus only on the Wigner-Ville distribution (WVD) that is most commonly used. The Wigner-Ville distribution of a signal is defined as [4]: ) (t s