# Extraction of Flaw Signals from the Mixed 1-D Signals by Denoising Autoencoder
## 1 Introduction  
Recently, the nondestructive evaluation (NDE) market worldwide has been growing rapidly due to strict regulations on public safety and product quality, continuous advances in electronics, automation, and robotics, high adoption rates of Internet of Things (IoT) devices, and the need to evaluate aging equipment [1]. NDE is a crucial and efficient method for evaluating structural integrity because it does not harm or damage the inspected subject. Among various NDE techniques, ultrasonic testing (UT) is the most commonly used technique, accounting for 31.10% of the market share [1]. While UT is a versatile NDE method, it is commonly used for inspecting welds in pipes or storage tanks and requires a skilled inspector for accuracy because the results highly depend on the inspector'scapabilities [2]. The main obstacle in using UT is that the method is highly sensitive to the testing environment and geometry of the testing material. UT uses the propagation of ultrasonic waves in a testing material, and the ultrasonic beams are reflected, deflected, and attenuated by internal flaws or the molecular structure of the testing material. The ultrasonic waves are also diffracted by the internal or external geometry and weld interface of the object, and these indications, other than those from flaws, are called background noise. Evaluating UT mainly involves distinguishing between the reflected ultrasonic waves that indicate flaws from those that do not. This evaluation requires the capability of the inspector. When there are too many indications other than the flaw indication, the evaluation becomes much more complex. Typically to overcome the obstacle, the Hilbert transform or the threshold setups are used. The most frequently used method of getting rid of the random noise is the threshold setup. However, the threshold setup only is useful when used in the field because it is only a method of showing signals that exceed the reference level, rather than removing noise or extracting the flaw signal. Additionally, the Hilbert transform is a phase-shifting method that multiplies h(\(f\)) to a signal in the frequency domain. This method is not a frequently used method since it has a chance of distortion of the original signal due to the multiplication. There still does not exist a clear solution to getting rid of random noises. An autoencoder is an artificial neural network (ANN) often used to extract input features. A denoising autoencoder (DAE) is one of the derivative models of the autoencoder that adds or eliminates random noise from the input to extract prominent features. DAE is already widely used for denoising noisy image or sound data. [PERSON] et al. [3] used the autoencoder technique to enhance ultrasonic classification performance. [PERSON] et al. [4] used DAE to denoise medical images, and [PERSON] et al. [5] used DAE for the reconstruction of the clean speech spectrum from reverberant speech. [PERSON] et al. [6] proposed a derivative of DAE called noise learning-based denoising autoencoder (nIDAE) to enhance the denoising performance of neural networks. [PERSON] et al. [7] denoised seismic random noise to enhance the signal-to-noise ratio of seismic data using DAE. Since DAE is often used to denoise noisy images or signal datasets, its implementation to the ultrasonic 1-D signal, known as ultrasonic A-scan, should be effective. Using DAE, it would be possible to get rid of random noise and extract the flaw signal from the mixed ultrasonic A-scan signal. Figure 1 shows an example of a mixed 1-D ultrasonic signal with a flaw indication and background noise. (a) The figure shows the region where the initial signal and wedge noise exist. (b) The figure presents the flaw signal, which is the target signal of this study. (c) The figure shows the welding interface signal and the geometrical signal from the specimen. As shown in Figure 1, an A-scan is a mixture of various signals from the UT procedure.