A Fuzzy Learning Approach for Identification of Arbitrary Crack Profiles Using ACFM Technique

This paper presents a new inversion approach to estimate the depth profile of a fatigue crack from the output signal of an alternating current field measurements (ACFM) probe. We first show that the mapping between the ACFM signals and corresponding crack depth profiles can be expressed by an affine transformation, then we utilize a generalized version of the fuzzy alignment algorithm (FAA) to find this mapping more efficiently. The proposed method is then extended using the Dave's objective function previously proposed for the fuzzy clustering. To evaluate the accuracy and robustness of the proposed inversion technique we reconstruct cracks of arbitrary shapes from both simulated and measured ACFM signals. The results show that the proposed approach is more robust and accurate than existing methods and gives reasonable results even in the absence of sufficient training data.