It is necessary to ensure appropriate information content of the measuring instruments used for intelligent diagnosing systems of energy and technological complexes based on the measurement of dynamic parameters. Sensors and measuring equipment should possess sufficient accuracy, reliability, speed and consistency of performance. Types of sensors for measuring dynamic parameters are selected depending on the systems structure. They can be, for example, sensors for the electrohydromechanical systems of these complexes, pressure sensors, as well as sensors of flow and temperature of the working media, displacement of moving elements and vibration of the base members. The type of sensor intended for use in the diagnostic system is largely determined by the dynamics of the processes taking place in it. It is necessary that the sensors satisfy their performance requirements. If the sensors have lower speed than is necessary for the process dynamics in the electrohydromechanical system, it can lead to dynamic measurement error and an error in the diagnostics of technical condition. In technical literature, the requirement for the sensor speed is indicated by the fact that it should be an order of magnitude higher than the dynamics of the processes occurring in the system. This approach is not acceptable for choosing the type of sensors for diagnostic systems, taking into account the process dynamics. Firstly, sensors for measuring with this required parameter may not be available. Secondly, even if there is a sensor with a parameter close in speed to the dynamics of the system processes, it is necessary to know in advance what dynamic error it can lead to and how it will affect the accuracy of the diagnostic system. An analytically generalized dependence of the dynamic measurement error of electrohydromechanical system parameters on the relative sensor speed is obtained in this paper. This dependence allows you to choose a sensor with a dynamic error that does not exceed a given value. The calculation of the dynamic measurement error is shown using the MI-8 helicopter hydraulic system as an example.
This article discusses a sampling algorithm for machine learning in order to capture the trend of the cumulative deterioration of the characteristics of a hydraulic pump (cumulative degradation), which affects the efficiency of its operation and manifests itself in the form of a drop in volumetric efficiency. To generate data, a simulation model of a typical station for the supply of working fluid in technological complexes, developed in the SimulationX program, is used. The transient processes of pressure change in the system are described, from the analysis of which a tendency of a decrease in the average component of the pressure signal is traced, which is used as a diagnostic feature - an indicator of the state of the system. An example is also considered that describes the possibility of assessing the residual life of the system based on data characterizing the past state of the system, and can be adapted when forming a more complex base, taking into account the use of artificial neural networks.
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