ELECTRIC GATE LIFTING MECHANISM TO DRAIN THE DAM WITH CONTROLLED PROCESS OF STARTING

Abstract

Currently, the important issues for the implementation of pre-training, pick-up and start-up sufficiently broad class technological mechanisms are characterized by a complex process of formation of resistance since the initial pick-up time. The example of such a mechanism is electric industrial twin engined electric lifting mechanism shutter to drain the dam. The system of "electric lifting mechanism—gate—pocket drainage dam" is complex and consists of a sufficient number of interacting subsystems, and the state of the system depends on the state of its elements, transition system from one state to another can occur implicitly, continuously or abruptly. Existing systems of controlled electric drive does not satisfy all the requirements of the manufacturing process as well as efficient and trouble-free operation regimes of electric technological mechanism. This is the basis for finding technical solutions that ensure the required quality of the process with minimal power consumption and reduce operating costs through the use of modern techniques and automation. The proposed expert system with fuzzy logic implemented on the two fuzzy-regulators on heuristic rules under the weight parameters input signals form the signal job frequency and voltage source of nourishment for the optimal frequency management law. In the analysis of research on the mathematical model there has been found that the processes of movement start and start-up of the proposed system, unlike systems controlled pick-up of rigidly-defined law of frequency management is characterized by longer duration of the transition process, but the current value and, consequently, power consumption acquire their maximum values only at a short time, which saves resources and drive motor system as a whole by reducing heat and vibration loads. The proposed expert system controlled pick-up can be improved by extending the heuristic rules with additional parameters that describe the state of the electromechanical system, and implementation based on neural networks.