A novel information entropy approach for crack monitoring leveraging nondestructive evaluation sensing

The accurate detection of crack activity is crucial for both material performance evaluation and structural damage assessment. Recent developments have found Shannon's Information Entropy method advantageous for characterizing damage in materials and applicable to real-time damage detection. This manuscript introduces a novel approach to crack detection that leverages Nondestructive Evaluation (NDE) datasets. Specifically, it introduces a discretization parameter to represent signal distributions and leverage information entropy to identify the onset of crack initiation . This approach enhances the reliability of material diagnostics by being applicable to multi-modal datasets thus improving the generalizability of the method. Moreover, by using raw sensing information and integrating information entropy with outlier detection , this approach reduces computational burden for real-time applications related to Mode I loading. To demonstrate this, lab experiments were conducted using compact-tension specimens of aluminum alloy and Acoustic Emission signals were collected to demonstrate the validity of the presented approach. Additionally, to investigate an extension of the approach, it was adopted to the case of image datasets from cyclic loading experiments, showcasing its potential for use with different NDE sensing methods and complex loading. The results were verified and compared with other signal processing methods such as machine learning. Furthermore, the approach’s potential for real-time monitoring systems is also discussed.