## 1 Introduction  
In recent decades, coal-fired power plants have been widely used as the main energy plants for large-scale industrial procedures. A coal-burning power plant boils water in boiler tubes using the heat generated by burning fossil fuels to provide high-pressure and high-temperature steam inside the tube. Boilers are critical units for transferring heat from the fuel to the water. Due to its extremely high operating pressure and temperature, the boiler tube is the component that most commonly undergoes failure [1; 2; 3; 4; 5]. Tubes wear out and can no longer sustain the high temperatures and pressures. Undiscovered failure in a tube may lead to even more destruction in other tubes [6]. The failure of boiler tubes can be caused by fuel ash particles, overheating, waterside and fireside corrosion fatigue cracking, stress corrosion cracking, erosion, etc., resulting in leakage in the tubes. Leakage in a steam boiler not only diminishes the total efficiency of the power plant operation due to unscheduled outages but also induces hazardous conditions for operators. Leakage can lead to catastrophic secondary damage, such as tube explosions and destruction of the whole boiler, if not handled properly. Condition monitoring and leak diagnosis of boiler tubes are indispensable for efficient performance and maintenance; they can help improve the reliability, safety, and availability of the system. In addition, a fault detection system is able to identify a fault at its earliest degradation phase by exploiting complex and non-stationary patterns of variables, enabling operators to take proper actions in advance. In a thermal power plant, measures such as forecasting of tube explosions, timely leak detection, and localization of leak positions are necessary to schedule repair times and minimize financial losses. However, the structure of a boiler is so complicated that the state of the component-tube interaction associated with the turbulence of the two-phase flow (water and steam) is hard to model. Hence, boiler tube maintenance can rely on data-driven fault detection in the tubes. Data, which indicate the health states of the tube, can be acquired by acoustic emissions, the electrical resistance, and vibration and ultrasonic signals [7; 8; 9], and the leakage detection mechanism is repurposed as a classifier to perform detection. Among these methods, acoustic emission (AE) is widely used to collect data to monitor a degrading system [9; 10; 11]. When a leak occurs in a tube, turbulence flow created by escaping fluid creates pressure waves throughout the escaping fluid, within the fluid itself, and within the container structure. These waves are related to structure-borne acoustic waves. To detect leaks, the energy associated with turbulence waves is transformed into electrical signals using different types of transducers, which are connected to a computer. AE sensors, which have high sensitivity, can record emission events caused by slight variations in the structure of a tube component [12]. An advantage of using AE sensors for assessment is that this allows the entire machine structure to be monitored simultaneously with a simple in situ set up. This analysis technique does not need to scan individual components but utilizes a set of sensors that are attached outside of the tube component to transmit AE signals. Several dynamic variables can then be simultaneously observed during a leakage process. Previous studies related to data-driven monitoring of boiler tubes mostly used manual processes to analyze collected data [13; 14]. These methods are executed by visualizing sudden variations in the recorded data and then manually detecting the leakage source. However, the highly complex and nonlinear signals acquired from the system when a leak occurs in the boiler tube are impossible to identify by simply observing the AE waveforms. Recently, meticulous artificial intelligent (AI) techniques have been investigated for leak detection in boiler tubes. For example, [PERSON] et al.