PSI - Issue 39

Nabam Teyi et al. / Procedia Structural Integrity 39 (2022) 608–623 Author name / StructuralIntegrity Procedia 00 (2019) 000–000

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detection. Huo et al. (2018) introduced Selfadaptive Entropy Wavelet (SEW) to identify transverse fracture cracks in rotating shafts. Using impulse modelling, CWT was used to breakdown a signal into multi-scale wavelet coefficients. For various shaft conditions, Shannon entropy was employed to derive dominating features from vectors. The SVM was used to classify faults and grade fracture defects. The proposed method was evaluated on a test rig and found to be successful in detecting and identifying cracks on rotating shafts. The RBF-based SVM achieved 99.3% classification accuracy. As a result, the proposed technology should be extended to additional rotating components in industrial rotating machinery. 5. Conclusion Cracks are the most critical physical fault in structural members such as beams and motion transfer members such as shafts. Shafts and beams are essential components of any industrial system. Before such beams or shafts are put into the assembly area to be fitted into the machines, they must pass testing to ensure that they are fit for usage. They are subjected to NDTs, and the first section of this paper includes a brief review of published studies in this area. Fatigue cracks are induced owing to cyclic loading conditions during operation after NDTs passed shafts and beams are assembled in machineries. As a result, such parts must be evaluated for crack analysis in order to avoid a costly shutdown due to machinery failure. Identification, localization, and assessment of cracks are all part of crack analysis. The machines in use give vibration data for these members, which are referred to as the system's signal data or generated dataset. To identify and find cracks, as well as estimate their severity, this dataset is evaluated using various data science technologies. A section of this study discusses the many types of cracks that can form in shafts and beams. The main goal of this study, however, is to give an overview of the numerous data science tools, techniques, and methodologies employed in crack characterization of cracked shafts and beams. The review is organised thematically into the many data science tools. The review portion begins with a brief overview of the technique, illustrated with a diagram, and then reports on selected literatures that used that technique. ANN, fuzzy logic, GA, EA, CEA, PSO, CWT, DWT, ANFIS, and ML models like DT, RF, and SVM were among the approaches explored. Extra tools such as PPCM, AIS, and SEW were also mentioned. Each of them has their own distinct approach to problem solving, and the choice is based on the type of input data available and the intended outcome. For better outcomes, many authors have blended a few of them. Acknowledgements The authors like to express their gratitude to their affiliated institute for providing the essential assistance in gaining access to journal articles for this review. Declaration of interests The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. Cheng Y., Tian L., Yin Y., Huang x., Cao J., Bai L., 2018. Research on Crack Detection Applications of Improved PCNN Algorithm in MOI Nondestructive Test Method, Neurocomputing, 277, 249–259. https://doi.org/10.1016/j.neucom.2017.02.099 Yusaa N., Uchimoto T., Takagi T., Hashizume H., 2012. An Accurately Controllable Imitative Stress Corrosion Cracking For Electromagnetic Nondestructive Testing And Evaluations, Nuclear Engineering and Design, 245, 1–7. https://doi.org/10.1016/j.nucengdes.2012.01.022 Hamia R., Cordier C., Dolabdjian C., 2014. Eddy-Current Non-Destructive Testing System for the Determination of Crack Orientation. , NDT & E International, 61, 24–28. https://doi.org/10.1016/j.ndteint.2013.09.005 Sharma D.S., Mungla M.J., Barad K.H., 2015. Vibration-Based Non-Destructive Technique To Detect Crack In Multi-Span beam. , Nondestructive Testing and Evaluation, 30, 291–311. https://doi.org/10.1080/10589759.2015.1029475 Si Y., Rouse J.P., Hyde C,J., 2020. Potential Difference Methods For Measuring Crack Growth: A Review. , International Journal of Fatigue, 136, 105624. https://doi.org/10.1016/j.ijfatigue.2020.105624 Nasiri S., Khosravani M.R., Weinberg K., 2017. Potential Difference Methods For Measuring Crack Growth: A Review, Engineering Failure References