## 1 Introduction  
Infrared thermography (IRT) is one of the many diagnostic methodologies currently applied in various types of industry and research [1]. It can be used to detect so-called invisible defects in a non-destructive way. Compared to traditional areas of IRT applications, such as biomedical applications [2], energetics, buildings, and other constructions [3; 4; 5; 6], the application of this methodology in aviation is still at an early stage [1]. The advantages of IRT have been fully demonstrated in pre-flight and post-flight aircraft checks, where damage (e.g., water in honeycomb structures of composite parts), is monitored by thermal imaging cameras [7; 8]. In aviation, extensive research and development is being conducted on the IRT diagnostics of corrosion in aluminum materials [9; 10], and especially composite structures [11; 12; 13]. In aircraft operation, it is possible to use IRT to detect air leaks inside the cabin [14], abnormal strain in individual parts of aircraft casings, fuselage, and wheel attachments [15], damage to electrical wiring and insulation [16; 17], etc. In aviation, IRT is also used for rescue, firefighting, and surveillance operations [18; 19]. Other applications of IRT diagnostics can be found in the area of unmanned vehicles, whose thermal imaging cameras are used to inspect power lines [20], pipelines [21], buildings [22], constructions [23], vegetation [24], etc. Thermography has also recently found an application in the design of stealth aircraft [25]. For the diagnostics of aviation engines, IRT is mainly used to check their composite covers [26]. Another area where IRT is applied, which is closer to the topic of this paper, is the diagnostics of turbojet engines; infrared images of the diagnosed engines' gas turbines are compared to standard IRT images of engines without defects, which allows temperature field anomalies to be detected and thus allows defects to be identified [27]. These defects can be damage to the turbine blades, combustion chamber disturbance, disturbance to the homogeneous field of fuel burning, etc. [28]. Aircraft engine diagnostics and health monitoring [29; 30; 31] can be divided into two basic processes: ground diagnostics and on-board diagnostics with their associated systems. On-board, or online, diagnostics involve the checking of engine components and thermodynamic processes during the operation of the engine [32]. These components include individual engine parts, such as compressors, combustion chambers, and turbines and associated accessories, while thermodynamic process checks include reviewing parameters such as temperature, pressure, rotational turbo-compressor speed, and vibrations. Ground, or offline, diagnostics [33; 34] involve tribo-technical analysis, non-destructive checking methods, and checks by thermal cameras as mentioned earlier. This article aims to develop a progressive method for the online evaluation of the technical state of a turbojet engine using infrared thermal imaging. Contrary to traditional methods, in which a comparative analysis of thermal images is performed on some normalized samples, this paper proposes an intelligent adaptive method which can discriminate anomalous areas of thermal images and create resulting diagnostic information automatically without any human input or previous training. Since the output of a thermal imaging camera is a digital image, computer vision techniques and methodologies should be used to automatically process this output. In order to extract meaningful information from a thermal image for a diagnostic system, a pattern recognition/classification approach has to be applied. Many methodologies and an array of methods can be used for this purpose [35; 36]. These include traditional methodologies, which perform pattern recognition using deterministic image-processing methods such as filtering, feature analysis, and support vector machines. [37]. However, the most successful methodologies for image classification come from the area of computational intelligence.