Methods of clustering parameters in the creation of neural network multi-mode dynamic models of aircraft engines

The presence on modern aviation gas-turbine engines of dozens and even hundreds of sensors for continuous registration of various parameters of their operation makes it possible to collect and process large amounts of information. This stimulates the development of monitoring and diagnostic systems. At the same time the presence of great volumes of information is not always a sufficient condition for making adequate managerial decisions, especially in the case of evaluation of the technical condition of aviation engines. Thus it is necessary to consider, that aviation engines it is objects which concern to individualized, i.e. to such which are in the sort unique. Therefore, the theory of creating systems to assess the technical state of aircraft engines is formed on the background of the development of modern neural network technology and requires the formation of specific methodological apparatus. From these positions in the article the methods which are used at carrying out clustering of the initial information received at work of modern systems of an estimation and forecasting of a technical condition of aviation gas-turbine engines are considered. This task is particularly relevant for creating neural network multimode models of aircraft engines used in technical state estimation systems for identification of possible failures and damages. Metric, optimization and recurrent methods of input data clustering are considered in the article. The main attention is given to comparison of clustering methods in order to choose the most effective of them for the aircraft engine condition evaluation systems and suitable for implementation of systems with meta-learning. The implementation of clustering methods of initial data allows us to breakdown diagnostic images of objects not by one parameter, but by a whole set of features. In addition, cluster analysis, unlike most mathematical-statistical methods do not impose any restrictions on the type of objects under consideration, and allows us to consider a set of raw data of almost arbitrary nature, which is very important when assessing the technical condition of aircraft engines. At the same time cluster analysis allows one to consider a sufficiently large volume of information and sharply reduce, compress large arrays of parametrical information, make them compact and visual