Machinery structural crack damage detection based on acoustic signal with ICEEMDAN and sensitive IMF fuzzy entropy

Abstract With the increasingly urgent demand on the reliability of mechanical equipment, in the process of production and service, it is of vital importance to apply precise and efficient crack damage detection on the critical structures. To overcome the shortcomings of existing damage detection methods and meet the urgent needs of engineering practice, by analyzing acoustic signal, a novel machinery structural crack damage detection method based on improved complete ensemble empirical mode decomposition with adaptive noise (ICEEMDAN) and sensitive intrinsic mode function (IMF) fuzzy entropy is proposed in this paper. Firstly, redundant second-generation wavelet denoising strategy based on neighborhood correlation is applied on the raw acoustic signal. Then, the pre-denoised acoustic signal is decomposed by ICEEMDAN to obtain a set of IMFs, and the fuzzy entropies of the first eight IMFs are calculated to reflect the structural crack damage states. Finally, with distance evaluation technique, the most sensitive IMF fuzzy entropy is selected and defined as the damage index to assess the structural crack damage levels. Effectiveness of the proposed method is validated by two case studies as for the crack damage detection of different machinery structures. The results show that the defined damage index is not only sensitive to the occurrence of structural crack damage, but also decreases obviously with the increasing damage level and is not affected by damage location.