The experimental steel rail was of UIC 54 type, which is the steel rail commonly used in countries in the Southeast Asian region. The elemental composition of UIC 54 Steel Rail was analyzed by handheld laser-induced breakdown spectroscopy (LIBS) analyzer (SciAps, model Z-300). Table 1 tabulates the elemental composition of UIC 54 steel rail.
The Hsu–Nielsen source was used as the artificial source of AE signal of crack, consisting of 2H pencil lead (0.5 mm), guide tube, and mechanical pencil. The pencil lead was broken against the head, web, and foot of steel rail at 30°, in accordance with the American Society for Testing and Materials (ASTM E976) standard.
The AE sensor amplitude was analyzed prior to capturing the AE signals of cracks in the steel rail to determine the sensor sensitivity and sensor coupling. The AE sensor amplitude testing was carried out at the rail head by breaking 2H pencil lead (0.5 mm) against the steel rail. The testing was performed five consecutive times and results averaged.
The digital signal data of PLB at the head, web, and foot of steel rail were datasets for training and testing the proposed deep learning algorithmic model. PLB were carried out 150 times each at the head, web, and foot of steel rail; and the AE signals were captured, totaling 450 AE signals ( Figure 7 ).
The un-denoised AE signals were subsequently divided into two groupings (150 and 300 AE signals) to investigate the effect of number of input data on the classification accuracy of the deep learning algorithmic model. Under the first grouping (150 AE signals), the input data were divided into a training dataset (80% of the input data) and a testing dataset (20%). Under the second grouping (300 AE signals), 80% of the input data were training dataset and the rest (20%) were testing dataset.
Figure 8 a–c shows the un-denoised AE signals (prior to pre-processing) of PLB at the head, web, and foot of steel rail. The AE signals of PLB were captured by the AE sensor and converted by the AE acquisition module into un-denoised AE digital signal data prior to pre-processing by total variation denoising (TVD) algorithm to remove the ambient noise.Figure 9 illustrates the conversion procedure of denoised AE signals (after pre-processing) of PLB into feature datasets (input data) for training and testing the deep learning algorithmic model. The training and testing datasets were transferred to a spreadsheet. The feature datasets were divided into two groupings (150 and 300 feature datasets) to investigate the effect of number of input data on the classification accuracy of the deep learning algorithmic model.Under the first grouping (150 feature datasets), 50 datasets each belonged to PLB at the head, web, and foot of steel rail. The feature datasets were divided into training dataset (80%) and testing dataset (20%). Under the second grouping (300 feature datasets), 100 datasets each belonged to PLB at the head, web, and foot of steel rail. Likewise, 80% of the input data were training dataset and the rest (20%) were testing dataset. Given the sampling rate of 20 MHz of the AE acquisition module, one feature dataset (i.e., one denoised AE signal) contained 300,000 data points.
In this research, one AE signal contains 300,000 data points, and the time to capture one data point is 0.05 µs, given fs = 20 MHz. As a result, the time required to capture one AE signal (i.e., 300,000 data points) is 0.015 s (= 300,000 × 0.05 × 10−6).
In Figure 10 , one row of yellow grid cells represents one AE signal (300,000 data points), and the total number of rows (i.e., N of Dataset) represents 450 AE signals (PLB digital signal data = 450 signals) at the rail head, web, and foot. In the columns “Target”, the rail head, rail web, and rail foot are represented by red, green, and blue colors, respectively.
In training the deep learning algorithm, a specific target (either rail head, web, or foot) was assigned to each row of yellow grid cells (i.e., each AE signal) using one-hot encoding, where 1 denotes a 100% probability and 0 a zero probability. The training was carried out separately for the first grouping (80% of the input data = 120 signals) and second grouping (80% of the input data = 240 signals) of AE signals.
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. In testing the algorithm, there were 30 signals (20% of the input data) for the first grouping, consisting of 10 signals each for rail head, web, and foot. Meanwhile, there were 60 signals (20% of the input data) for the second grouping, consisting of 20 signals each for rail head, web, and foot.
The following example is to show the testing process of the algorithm, assuming that the deep learning-generated probability of Y1, Y2, and Y3 of the first row of yellow grid cells (AE signal#01) are 0.1 (10%), 0.4 (40%), and 0.5 (50%), the proposed deep learning algorithm would select Y3 (rail foot) as the location of the crack, based on the highest probability value.