## 1 Introduction  
Cylindrical vessels are more commonly used as pressure vessels than spherical vessels due to their ease of fabrication, space utilization, pressure resistance and ease of maintenance. In a confined space, cylindrical tanks are very effective for the storage and use of fuel gases due to their ease of transport, high storage capacity and pressure regulation. However, high-pressure gas cylinders and tanks are plagued by the risk of gas leakage due to container damage. The need for reliable maintenance has increased dramatically in recent years, because the most important way to prevent disasters caused by gas leaks is to detect and repair leaks at an early stage. Several leak detection methods have been proposed for the early detection of leaks in pipelines [1]. These include acoustic emission (AE) detection, flow rate or pressure monitoring, or infrared detection [2, 3, 4, 5, 6, 7, 8]. Among the various methods, AE methods have proven to be very effective in detecting and locating leaks by eliminating interfering signals through time and frequency domain analysis and characterization of AE parameters [2, 9, 10]. In general, the most notable methods for locating an AE source are the time domain technique and the zone location technique [11]. The former relies on the time difference of arrival (TDOA) between two or more AE sensors to derive the source location [12]. However, the time delay can also result from different wave paths, different wave modes due to the dispersion of the AE wave for a long-distance pipeline and the velocity variation due to the environment. Therefore, the TDOA technique requires high detection accuracy. However, because the TDOA method is applied to burst-shaped waveforms, it is not suitable for locating leak sources in the case of leaks that produce continuous AE waveforms. To overcome this, the cross-correlation algorithm is sometimes used to obtain the TDOA from continuous waveforms [12; 13; 14]. Due to background noise and other uncertainties during the measurement, achieving a good level of correlation coefficient is limited [9; 15]. In practical situations, the AE signal generated by a leak has an aperiodic waveform structure, and the exact location of the leak source cannot be determined by cross-correlation-based methods. The second one of the basic AE localization techniques is based on the attenuation of the AE amplitude with the distance between the leak source and the detection sensor, taking advantage of the fact that the sensor with the largest amplitude is closest to the leak source, assuming that the sensitivity of the sensors is the same. Basically, a TDOA-based source location is very difficult to apply to the case of low signal-to-noise ratio (SNR) AE signals. Recently, machine learning (ML) and deep learning (DL) techniques have been adapted to solve this problem. [PERSON] et al. used support vector machine (SVM) learning and relevance vector machine (RVM) pattern recognition algorithms to classify the healthy and leakage AE data for identification and localization of leakage in pipelines [16]. [PERSON] et al. modified the original residual neural network (ResNet) DL architecture to overcome the limitations of conventional TDOA-based source localization techniques [17]. [PERSON] et al. presented a novel acoustic emission (AE)-based pipeline monitoring approach with a customized one-dimensional dense convolutional network (DenseNet) architecture to achieve accurate leak detection and size classification with an average accuracy of 99.7% in energy pipeline networks [18]. They also introduced a hybrid DL technique by integrating the convolutional neural network (CNN) and the long short-term memory (LSTM) models, which transforms time domain AE signals into scalogram images. The technique resulted in accuracy rates of 99.69% [19]. However, the TDOA-based source localization methods have limitations in pinpointing the location of the leak in three dimensions because it ignores the influence of the AE signal due to the structural characteristics of the vessel.