## 1 Introduction
These include artificial neural networks, which have been widely and successfully applied in image processing, pattern recognition, and diagnostic tasks [38; 39]. The most complex neural networks, such as convolutional neural networks, are able to classify any pattern in an image with high precision, as well as detect objects [40]. However, these approaches are computationally complex and require large amounts of data and training [41]. In order to create a successful diagnostic method for pattern recognition in IRT images, a simpler methodology should be used, since such images are more homogeneous than traditional images; they have fewer colors, lower resolution, and are well structured, with well-defined edges. The motivation for this study was to develop a methodology which does not require a large amount of computationally intensive training to successfully segment infrared images using intelligent adaptive algorithms. Accordingly, in this paper, the field of neural networks with uncontrolled learning is explored. One of the methods that has been successfully used in pattern recognition and image segmentation is self-organizing feature maps (SOFMs), which comply with the demand for simplicity, uncontrolled learning, and good classification results. SOFMs can be transformed and formalized into the form of a Kohonen neural network [42; 43]. Kohonen neural networks have been successfully applied in image segmentation tasks, the detection of specific areas in geographic maps [44; 45; 46], and the detection of cracks and corrosion in materials [47]. The results obtained from these applications represent a solid foundation and an inspiration for this approach to be applied in the segmentation of infrared images for the detection of anomalies in the temperature fields of different objects. The results of pattern recognition and image segmentation come in the form of classes present in the image. In order to extract knowledge from this information, the classes have to be interpreted. This can be done by simply labeling the classes; however, in the field of technical systems, this may not be sufficient. In technical objects, some areas are naturally hot and some are naturally cold, and therefore there needs to be some inference system which interprets the displacement and size of the classes from thermal images, and generates diagnostic information, which in the simplest case can be the determination of whether the diagnosed object is in a normal or an abnormal operational state [48; 49]. The traditional approach is to consider a diagnosed system as healthy if it operates within some specific limits; otherwise, a failure is indicated [48; 49]. This is a natural approach, and can be used when dealing with a technical system where parameters which determine the technical state (temperature, vibrations, etc.) can be simply measured, such as a jet engine. However, to extract diagnostic information from a classified image, a more complex approach needs to be used [49; 50]. Diagnostic expert systems are a natural candidate for extracting diagnostic information from complex data, and have been applied in the diagnostics of turbojet engines [49; 50]. The aim of the present study is to create a specialized approach for the creation of a rule-base of an expert system which can transform class information in an infrared image into useful diagnostic information, thus creating hybrid architecture. The functionality of this approach has been evaluated and designed specifically for application to turbojet engines, and the approach can be considered as novel compared to other intelligent approaches in turbojet engine diagnostics, which are applied to data measured by the engines' sensors and do not deal with visual/infrared image information [51; 52; 53]. The aim of this paper is to explore the design of this new approach and prove its functionality by applying thermal vision-based diagnostics in an automated adaptive system in the area of turbojet engine diagnostics.