# Data Driven Leakage Detection and Classification of a Boiler Tube
## 1 Introduction  
Thermal power plants generate electrical power from heat energy. The heat energy is converted to electrical power mostly through steam-driven turbines. Steam is produced by heating the water with the help of heating sources, such as fossil fuel, nuclear heat energy, and solar heat energy. The steam generated spins a steam turbine that drives an electrical generator, thus producing electrical power that is then added to the grid through the main transformer [1; 2; 3]. Thermal power plants are becoming more successful due to technological advancements, and demand for them is increasing [4]. One reason for their success and increasing demand is their low construction cost and reduced transmission cost [5; 6]. The heat exchanger of the boiler in thermal power plants consists of thousands of tubes that transfer heat from one fluid to another [7]. Over the operation period, these tubes may develop defects that can cause fluid leakage. Leakage may be due to overheating, foreign matter flow, or tube wall deterioration due to machining. Such leakage affects power plant operation and can cause unexpected downtime of the plant with a longer outage and significant loss in power generation. In addition, such leakage can affect the adjacent components and sometime results in an explosion with the destruction of the whole boiler [8]. Moreover, with the failure of the system, other factors such as economic losses and safety risk are also possible. Thus, leakage detection is necessary to avoid all of the problems associated with it. One possible and efficient solution is to perform condition-based maintenance of the boiler tubes. This condition-based maintenance of the boiler tube is attained through data-driven condition maintenance (leakage detection) of the tubes. There are two main steps in the data-driven condition maintenance of an object: (1) data acquisition that contains valuable information about thecondition of the object; and (2) preprocessing and classification of the data acquired. If there is recorded information about the boiler tube, then it can be categorized whether a certain record belonged to the boiler tube when it was working in a normal condition or in a leakage state. Thus, the leakage detection can be considered and solved as a classification problem. The data on the health states of the boiler tube can be recorded in the form of ultrasonic signals, electrical resistance measurement, and acoustic emissions [9; 10; 11; 12; 13; 14; 15]. Out of these techniques, acoustic emission (AE) collection is popularly used to acquire data from the deteriorated objects. AE sensors, due to their high sensitivity, can record activity generated due to slight variation in the physical state of an object [16]. Therefore, information collection with the help of AE sensors about the condition of boiler tubes is a good choice. There is not enough research related to data-driven condition monitoring of the boiler tubes in thermal power plants. The existing works are mostly regard manual monitoring of the data collected from boiler tubes [17]. This is being done by visualizing the change in the collected data manually and then identifying the leakage manually. There is no intelligent process in place that can automatically identify when leaks appear in the tubes. The present study designed a system that can automatically identify whether a collected sample (AE signal) belongs to the normal or leakage state of the boiler tubes. The recorded AE signal from the boiler tube contains valuable salient information about the health sate of the boiler tube. When a leak appears on a tube, the characteristics of AE signals, that is, amplitude, frequency, and so on, also change accordingly. However, the signals recorded from the boiler tubes are highly complex and nonlinear. Therefore, leakage of the boiler tubes cannot be detected only by visualization of the AE waveforms. Furthermore, a classifier does not yield satisfactory results with raw AE signals as inputs.