## 2 Methodology  
### Using Infrared Imaging for Diagnostics  
Every object whose temperature is above absolute zero radiates thermal energy. IRT systems measure this energy to create thermal infrared images. These images can be used to identify heat radiation anomalies of an object, which are usually characterized by increases or decreases in the radiated temperature [8; 9; 13]. The basic principle of IRT diagnostics is shown in Figure 1, with the following general meaning:  
* Image: an object to be captured by a thermal camera. * Classification: monitoring the object, scanning it, obtaining infrared data, classifying hot and cold parts as well as possible problematic areas. * Diagnostic information: evaluating infrared thermal images, for example, identifying defects or deficiencies, deciding about the technical state of the engine. The aforementioned principle is a standard principle when using human expertise to obtain diagnostic information from infrared images. Examples of applications in aviation are pre-flight and post-flight engine checks, where the engine's casing is checked for defects and cracks caused by freezing water or material deficiencies. In such cases, the evaluation of thermal images and the detection of anomalies is strictly dependent on human expert knowledge [49]. When a human expert inspects infrared images of a technical object, he or she basically performs two operations. First, he/she discriminates different areas of the thermal image and classifies them into categories according to their temperature. They next use their expert knowledge to inspect hot or cold areas for some anomalies according to their position and temperature. The traditional approach is to superimpose the obtained infrared image over a standardized infrared image of an object without defects and evaluate the differences between the two images [54]. The whole process can therefore be formalized into two basic parts--image processing and classification, and knowledge acquisition. A variety of methods exist to solve both problems, as mentioned in the introduction, and are suitable to identify and evaluate anomalies. As previously stated, one aim of this study was to develop an autonomous diagnostic system which is able to learn and locate anomalies without any prior knowledge (e.g., normal faultless operation IRT images). The other aim was to make the system autonomous. The general concept of the intelligent diagnostic system can be expressed by the scheme shown in Figure 2. The data acquisition part of the approach is usually done using the infrared camera, and the image pre-processing can be done before the data are fed into a classifier. The effects of the image pre-processing and filtering are not directly investigated in this study; however, the methodology is designed to be relatively robust in order to use image data of reasonable quality with low noise levels. Infrared cameras usually pre-process the image data in order to compensate for air humidity and other environmental effects. The robustness is achieved by the application of an intelligent adaptive algorithm, and the design therefore consists mainly of the classifier part by finding a methodology and selecting its structure, input/output relations, and the training algorithm in the case of the application of neural networks. Information about classes in the thermal image can be further used to evaluate the technical state of the investigated object (in this case, a jet engine), and a diagnostic expert system is a natural candidate for the completion of this task. The basis of the proposed methodology is therefore centered around:  
* Image processing classification system: involving a SOFM. * Knowledge acquisition system: an expert system for the evaluation of the technical state of an engine. Figure 1: Procedure for the detection of temperature anomalies in engine casings by analyzing an infrared image.