Abstract
This research proposes a nondestructive single-sensor acoustic emission (AE) scheme for the detection and localization of cracks in steel rail under loads. In the operation, AE signals were captured by the AE sensor and converted into digital signal data by AE data acquisition module. The digital data were denoised to remove ambient and wheel/rail contact noises, and the denoised data were processed and classified to localize cracks in the steel rail using a deep learning algorithmic model. The AE signals of pencil lead break at the head, web, and foot of steel rail were used to train and test the algorithmic model. In training and testing the algorithm, the AE signals were divided into two groupings (150 and 300 AE signals) and the classification accuracy compared. The deep learning-based AE scheme was also implemented onsite to detect cracks in the steel rail. The total accuracy (average F1 score) under the first and second groupings were 86.6% and 96.6%, and that of the onsite experiment was 77.33%. The novelty of this research lies in the use of a single AE sensor and AE signal-based deep learning algorithm to efficiently detect and localize cracks in the steel rail, unlike existing AE crack-localization technology that relies on two or more sensors and human interpretation.
Highlights
Rail transport plays an important role in transferring a large number of passengers and freight between destinations
A computer was used for storageTotal of digital signal data. (TVD) algorithm was used to remove noise in the acoustic emission (AE) signal variation denoising of cracks in a steel rail
In testing the deep learning algorithm, a given AE signal was applied to the trained deep-learning algorithm and the algorithm classified the crack location based on probability, where Y1, Y2, and Y3 denote the head, web, and foot of steel rail, respectively
Summary
Rail transport plays an important role in transferring a large number of passengers and freight between destinations. The technology was utilized to detect and localize fatigue cracks in the steel rail and rail components under a load [35,36,37]. In [38], a single-sensor AE scheme based on image-based deep learning was proposed to localize defects in plate-like structures. Their proposed scheme utilized a single sensor, similar to this current research. This research proposes a nondestructive single-sensor AE scheme with AE signal-based deep learning algorithm for detection and localization of cracks in the steel rail under a load. The AE scheme was subsequently implemented onsite to detect cracks in the steel rail and a comparison of the resulting classification performance was conducted
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