Recently, hydro units are used in maneuverable modes, which leads to an increase in the load on all elements of hydro units. It can lead to unpredictable failure of their components and even failures in the operation of the hydroelectric power plant. Therefore, the use of diagnostic systems is appropriate for solving such problems. The most promising non-destructive diagnostic systems are vibration diagnosis systems. However, the effectiveness of their use depends on the diagnosis algorithms, which depend on the features and functioning of the diagnosed unit of the power unit. In this work, some models of vibration signals of hydraulic units and features of the construction of vibration diagnostics systems of hydrogenerator units are considered

Some peculiarities of constructing information channels, which are a part of multilevel information-measuring systems for diagnostics of electrical equipment, are considered. The primary attention in the report is paid to the consideration of primary diagnostic information measurement channels, as well as the block of training aggregates, where the infor-mation about defects and possible modes of operation of some electrical equipment units is stored. One of the possible options for building a primary measurement channel focused on using wireless measurement sensors, which are consis-tent with the use of international standards, has been considered. The description of diagnostic features for determining the technical condition and classification of possible defects in individual nodes of electrical equipment concerning their modes of operation is briefly described. Based on accepted diagnostic attributes, the models of representation of training sets, which correspond to different technical states of electrical equipment units for different modes of opera-tion, are considered. Ref. 10, fig. 3.

The results of consideration of advanced mathematical models of vibration diagnostic signals are given, which take into account both the properties of diagnostic objects and the modes (speed, electric temperature, etc.) in which the object under study operates. Models of representation of training sets corresponding to certain technical states of EO units and which can work in different modes are considered. The method of representation of training sets in the form of a matrix which elements represent scattering ellipses corresponding both to certain kinds of defects of separate knots of EO, and modes of its work is offered. The structure of construction of training sets on a flat (2D) and volume (3D) matrix is ​​substantiated, the elements of which contain sets corresponding to separate units of EA, and their combination forms a separate electrotechnical unit. References 18, figures 5.

Досліджено особливості використання лінійних AR та ARMA процесів в якості математичних моделей вібраційних сигналів двигунів власних потреб ТЕС і ТЕЦ та двигунів вітрогенераторів.Визначено особливості побудови прототипу безпровідної інформаційно-вимірювальної системи діагностування стану таких двигунів

Розроблено та апробовано метод і математичні моделі прямих та обернених задач ультразвукового контролю та діагностики складних металевих конструкцій на наявність дефектів.Виготовлено та експериментально перевірено дослідний зразок системи магнітострикційного

Наведено основні вимоги до систем діагностування електроенергетичного обладнання з урахуванням концепції Smart Grid.Розглянуто деякі результати, що стосуються питань утворення вібраційних діагностичних сигналів у окремих вузлах електротехнічного обладнання за допомогою ударних впливів.Запропоновано основні складові до інформаційного забезпечення для систем діагностики електротехнічного обладнання, беручи до уваги основні вібраційні складові, що утворюються за

White Noise in Some Simulation Problems of Information Signals*À ñonstructive method of information signal mathematical models characterization on the white noise basis is developed.Linear random processes, linear random processes with periodic structures, linear autoregressive processes, linear autoregressive processes with periodic structures are represented as examples of the method application.K e y w o r d s: white noise, linear random process, linear random process with periodic structures, linear autoregressive processes.