WINSAS: A New Tool for Enhancing the Performance of Eddy Current Inspection of Aging Aircraft Wheels

Eddy current techniques are widely used in the inspection of aging aircraft. A commercial eddy current system manufactured by ANDEC is currently in use in several airline companies including: Northwest, USAir, Canadian, Lufthansa and Delta airlines. In operating this system, the eddy current probe is moved vertically while the wheel is rotated horizontally, resulting in a helical scan of the wheel outer surface. Two probe types: high and a low frequency probes, are used simultaneously to allow detection of surface and subsurface cracks. A new tool: Wheel Inspection & Signal Analysis System (WINSAS), version 1.0 has been developed and integrated into ANDEC system. WINSAS runs on a PC with Input/Output card and controls the functions of data acquisition & storage, and signal display & analysis. Data is acquired of the impedance channels, coming out of an eddyscope at time intervals determined by an encoder installed on the shaft. Use of the encoder synchronizes the data acquisition process and makes the data display and processing invariant to variations in the shaft speed. WINSAS offers robust handling and interpretation of the eddy current signal. Visualization of the eddy current signal is enhanced using several display forms: electronic strip chart, complex impedance plane, A-scan, and C-scan images. Signal interpretation is also enhanced using WINSAS utilities such as user control of the image colormap and adjustment of the vertical and horizontal A-scan track positions. Automatic classification of the eddy current signal is available in WINSAS based on a neural network approach. Feedback comments obtained from airline company’s personnel pointed various advantages of adding the WINSAS tool. The user can easily locate features of the wheel on the electronic strip chart, while simultaneously following the trajectory signal of these features on the impedance plane window. C-scan images allow visualization of feature size and location on the wheel, in contrast to paper strip chart, which required experience in recognizing the number of flaws and flaw size and location. Automatic retrieval of old data of the wheel and using a display of difference images between the current and previous data, using image registration techniques can enhance the process of monitoring flaw initiation and growth. The use of automatic signal classification using neural network approach, to obtain on-line decisions about signal type, and then mark flaw locations on the wheel, expedite the process of wheel inspection and increase its reliability. The capability of remote control and monitoring of the inspection process, using a computer terminal on an inspector’s desk is appreciated. Automation of the process of aircraft wheel inspection would establish a common language for NDE personnel, where wheel data and images obtained at one part of the world, could get inspected and analyzed, immediately, thousands of miles away.