###### Abstract
[19] reported the feasibility of measuring hydrogen effusion in austenitic stainless steel (1.4301) using neutron radiography at a research reactor. [PERSON] et al. [20] non-destructively evaluated the damage and mechanical properties of hydrogen-filled stainless steel 304 using acoustic emission and ultrasound. [PERSON] et al. [21] employed the synthetic aperture focusing technique (SAFT) to describe HIC images, with location and size information close to the actual defects, and determined the presence or absence of stepwise cracks using a technique for identification of flaw signals using deconvolution (TIFD). As shown in these previous studies, radiography, acoustic emission, and ultrasound have been used for the NDE of LHSVs. Other techniques, such as eddy current testing, magnetic particle inspection, and liquid penetrate testing, can be applied non-destructively to detect defects in LHSVs [22]. Among the NDE techniques, ultrasonic testing has been widely used because of its advantages of being fast, safe, cost-effective, and easy to use [23]. However, the pulse-echo method, which uses one ultrasonic sensor, is unsuitable for LHSVs unless high-frequency ultrasound is used. This is because the shape of LHSVs is cylindrical, and the walls are usually \(\sim\)3 mm thick, depending on the working pressure [18,24], so there is a technical limit to applying the conventional ultrasonic pulse reflection method using a longitudinal or transverse wave. In addition, at high frequency, it is difficult to evaluate the defects suggested in the received signal obtained by the pulse-echo method because of its high acoustic impedance (about 50 kg/m\({}^{2}\) s). Guided waves (GWs) propagated within a geometric structure have been applied to a wide range of items in industrial fields (e.g., pipes, steam generator tubes, and thin plates) because of the superior characteristics of this approach for inspecting long-range specimens [25, 26, 27, 28, 29]. Unlike other ultrasonic testing methods, ultrasonic GWs can be used on all types of specimens without removing insulation or coatings [30, 31, 32]. In addition, because the acoustic attenuation is relatively small, it is not necessary to scan every part of the vessel, even when detecting defects in large vessels. Because there are several solutions to the propagation equation of ultrasonic GWs, various propagation modes can be found. Therefore, high-performance and high-efficiency results can be obtained by selecting the propagation mode most suitable for the test object. To improve the defect detection ability of the technique using guided ultrasound, short-time Fourier transform (STFT) was employed to analyze the guided ultrasound mode and linear discriminant analysis (LDA) for classification according to the presence or absence of defects. Signals in the time-frequency domain obtained using STFT displayed information on arrival times according to frequencies, and the dispersion patterns of each mode could be clearly displayed [33]. LDA is a pattern-classification method that is suitable for two-group classification, such as the presence or absence of defects [34; 35; 36]. Various signal processing methods, such as wavelet transform, Hilbert transform, and empirical mode decomposition, have been widely used in the field of ultrasonic NDT technologies. In this study, a new machine learning method based on LDA with GW data for classifying defective specimens from sound specimens was developed because the LDA-based classifier is a well-known pattern classifier, and LDA is able to discover a linear combination of features that distinguish or classify two or more classes of events or objects [35]. Therefore, by analyzing the signal properties implemented in the time-frequency domain, features were extracted from the pixel information in the described images. These were used to find a linear combination of features better able to separate the two classes. In this study, to develop an NDE method for LHSVs, ultrasonic GW techniques based on LDA and STFT were employed.