The steam turbine blades of low pressure stages are endangerd by the high-cyclic fatigue due to the combined loading of dynamic stresses by the steam time-variant pressure and the pre-stress from centrifugal forces. Therefore, the importance of their experimental dynamic analysis in the design stage is critical. For laboratory tests of the blades, the piezo actuators placed on the blades, unlike electromagnets placed in the stationary space, give a possibility to excite the flexural vibration of the blades within the bladed disk by time continuous forces independently of the rotor revolutions. In addition, the piezo actuators can be also used to control the vibrations of the blade. Therefore, several dynamic experiments of the clamped model blade equipped with PVDF films were performed for the force description of the piezo foils and their behavior as actuators of the blade vibration. The numerical beam models were used for numerical analysis of the vibration suppression effects both by additional parametric excitation and by active damping. The optimal phase shift of piezo actuator voltage supply was ascertained both for amplitude amplification and suppression. The results contribute to the knowledge of the actuation and active damping of blade vibration by the piezo elements
The paper deals with the application of the feed-forward and cascade-forward neural networks to mechanical state variable estimation of the drive system with elastic coupling. The learning procedure of neural estimators is described and the influence of the input vector size and neural network structure to the accuracy of state variable estimation is investigated. The quality of state estimation by neural estimators of different types is tested and compared. The simple optimisation procedure is proposed. Optimised neural estimators of the torsional torque and the load machine speed are tested in the open-loop and closed-loop control structure of the drive system with elastic joint, with additional feedbacks from the shaft torque and the difference between the motor and the load speeds. It is shown that torsional vibrations of the two-mass system are damped effectively using the closed-loop control structure with additional feedbacks obtained from the developed neural estimators. The simulation results are confirmed by laboratory experiments.
In this paper, the MFC sensor and actuators are applied to suppress circular plate vibrations. It is assumed that the system to be regulated is unknown. The mathematical model of the plate was obtained on the base of registration of a system response on a fixed excitation. For the estimation of the system’s behaviour the ARX identification method was used to derive the linear model in the form of a transfer function of the order nine. The obtained model is then used to develop the linear feedback control algorithm for the cancellation of vibration by using the MFC star-shaped actuator (SIMO system). The MFC elements location is dealt with in this study with the use of a laser scanning vibrometer. The control schemes presented have the ability to compute the control effort and to apply it to the actuator within one sampling period. This control scheme is then illustrated through some numerical examples with simulations modelling the designed controller. The paper also describes the experimental results of the designed control system. Finally, the results obtained for the considered plate show that in the chosen frequency limit the designed structure of a closed-loop system with MFC elements provides a substantial vibration suppression.