Combined Acoustic Emission and Digital Image Correlation for Early Detection and Measurement of Fatigue Cracks in Rails and Train Parts under Dynamic Loading
Alexander Machikhin,
Anton Poroykov,
Vladimir Bardakov,
Artem Marchenkov,
Daria Zhgut,
Milana Sharikova,
Vera Barat,
Natalia Meleshko,
Alexander Kren
Affiliations
Alexander Machikhin
Moscow Power Engineering Institute, 14, Krasnokazarmennaya Str., 111250 Moscow, Russia
Anton Poroykov
Moscow Power Engineering Institute, 14, Krasnokazarmennaya Str., 111250 Moscow, Russia
Vladimir Bardakov
Moscow Power Engineering Institute, 14, Krasnokazarmennaya Str., 111250 Moscow, Russia
Artem Marchenkov
Moscow Power Engineering Institute, 14, Krasnokazarmennaya Str., 111250 Moscow, Russia
Daria Zhgut
Moscow Power Engineering Institute, 14, Krasnokazarmennaya Str., 111250 Moscow, Russia
Milana Sharikova
Moscow Power Engineering Institute, 14, Krasnokazarmennaya Str., 111250 Moscow, Russia
Vera Barat
Moscow Power Engineering Institute, 14, Krasnokazarmennaya Str., 111250 Moscow, Russia
Natalia Meleshko
Moscow Power Engineering Institute, 14, Krasnokazarmennaya Str., 111250 Moscow, Russia
Alexander Kren
Institute of Applied Physics, National Academy of Sciences, 16, St. Akademicheskaya, 220072 Minsk, Belarus
Fatigue crack in rails and cyclic-loaded train parts is a contributory factor in multiple railroad accidents. We address the problem of crack detection and measurement at early stages, when total failure has not yet occurred. We propose to combine acoustic emission (AE) testing for prediction of crack growth with digital image correlation (DIC) for its accurate quantitative characterization. In this study, we imitated fatigue crack appearance and growth in samples of railway rail and two train parts by cyclic loading, and applied these two techniques for inspection. Experimental results clearly indicate the efficiency of AE in the early detection of fatigue cracks, and excellent DIC capabilities in terms of geometrical measurements. Combination of these techniques reveals a promising basis for real-time and non-destructive monitoring of rails and train parts.