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A correct diagnostics together with the early prediction of failure or malfunction of the system are the major issues in modern maintenance. Nowadays, the time-honored diagnostics may be inadequate and lead to omitting failures, resulting in higher costs of repairing damaged equipment. That is why the interest in intelligent diagnostics increases due to the possibility of better interpretation of the component status and early failure prediction. One of the ways of determining the device condition is to measure and analyze the temperature of multiple points on the device. Thermographics may show a beginning of significant wear of a component, and enable a repair or replacing before the failure appears. The paper presents the key aspects of the diagnostics with thermal images, including: technology mapping, mapping algorithms, together with a presentation of available software solutions.Index Terms -intelligent diagnostic, mapping, thermography.

Proper maintenance of the electricity infrastructure requires periodic condition inspections of power line insulators, which can be subjected to various damages such as burns or fractures. The article includes an introduction to the problem of insulator detection and a description of various currently used methods. Afterwards, the authors proposed a new method for the detection of the power line insulators in digital images by applying selected signal analysis and machine learning algorithms. The insulators detected in the images can be further assessed in depth. The data set used in the study consists of images acquired by an Unmanned Aerial Vehicle (UAV) during its overflight along a high-voltage line located on the outskirts of the city of Opole, Opolskie Voivodeship, Poland. In the digital images, the insulators were placed against different backgrounds, for example, sky, clouds, tree branches, elements of power infrastructure (wires, trusses), farmland, bushes, etc. The proposed method is based on colour intensity profile classification on digital images. Firstly, the set of points located on digital images of power line insulators is determined. Subsequently, those points are connected using lines that depict colour intensity profiles. These profiles were transformed using the Periodogram method or Welch method and then classified with Decision Tree, Random Forest or XGBoost algorithms. In the article, the authors described the computational experiments, the obtained results and possible directions for further research. In the best case, the proposed solution achieved satisfactory efficiency (F1 score = 0.99). Promising classification results indicate the possibility of the practical application of the presented method.