Using AC field measurement data at an arbitrary liftoff distance to size long surface-breaking cracks in ferrous metals

We propose a model-based inversion method to size long surface-breaking cracks in ferrous metals using alternative current field measurement (ACFM) data at an arbitrary liftoff distance. This method employs conjugate gradients optimization to invert measured surface ACFM signal to crack depth. To alleviate the adverse effect of sensor liftoff uncertainty on crack sizing, we propose a blind de-convolution algorithm for reconstructing respective surface ACFM crack signal. In this algorithm, the partially known filter function associated with the sensor liftoff is estimated from which the surface crack signal can be restored. The validity of the proposed inversion method is demonstrated by comparing the actual and predicted depths of several simulated and machine-made long cracks in mild steel test blocks.