Beams with rectangular cross-section, with large length-to-width ratio, can be excited to torsional vibrations. If the piezoelectric elements are mounted to the beam in pairs at the same cross-section with two separated elements positioned on the same side of the beam, and the voltages applied to them are in the opposite phase, they produce twisting moments which can be applied to reduce the torsional vibrations. Results of FEM simulations are presented and analysed in the paper. All analyses are performed for a steel free-clamped beam. The piezoelectric elements made of PZT material are mounted in pairs on one side of the beam. The analyses are done for separated natural modes.

Many industrial rotating machines driven by asynchronous motors are often affected by detrimental torsional vibrations. In this paper, a method of attenuation of torsional vibrations in such objects is proposed. Here, an asynchronous motor under proper control can simultaneously operate as a source of drive and actuator. Namely, by means of the proper control of motor operation, it is possible to suppress torsional vibrations in the object under study. Using this approach, both transient and steady-state torsional vibrations of the rotating machine drive system can be effectively attenuated, and its precise operational motions can be assured. The theoretical investigations are conducted by means of a structural mechanical model of the drive system and an advanced circuit model of the asynchronous motor controlled using two methods: the direct torque control – space vector modulation (DTC-SVM) and the rotational velocity-controlled torque (RVCT) based on the momentary rotational velocity of the driven machine working tool. From the obtained results it follows that by means of the RVCT technique steady-state torsional vibrations induced harmonically and transient torsional vibrations excited by switching various types of control on and off can be suppressed as effectively as using the advanced vector method DTC-SVM.

The paper describes a novel online identification algorithm for a two-mass drive system. The multi-layer extended Kalman Filter (MKF) is proposed in the paper. The proposed estimator has two layers. In the first one, three single extended Kalman filters (EKF) are placed. In the second layer, based on the incoming signals from the first layer, the final states and parameters of the two-mass system are calculated. In the considered drive system, the stiffness coefficient of the elastic shaft and the time constant of the load machine is estimated. To improve the quality of estimated states, an additional system based on II types of fuzzy sets is proposed. The application of fuzzy MKF allows for a shorter identification time, as well as improves the accuracy of estimated parameters. The identified parameters of the two-mass system are used to calculate the coefficients of the implemented control structure. Theoretical considerations are supported by simulations and experimental tests.

Variation in powertrain parameters caused by dimensioning, manufacturing and assembly inaccuracies may prevent model-based virtual sensors from representing physical powertrains accurately. Data-driven virtual sensors employing machine learning models offer a solution for including variations in the powertrain parameters. These variations can be efficiently included in the training of the virtual sensor through simulation. The trained model can then be theoretically applied to real systems via transfer learning, allowing a data-driven virtual sensor to be trained without the notoriously labour-intensive step of gathering data from a real powertrain. This research presents a training procedure for a data-driven virtual sensor. The virtual sensor was made for a powertrain consisting of multiple shafts, couplings and gears. The training procedure generalizes the virtual sensor for a single powertrain with variations corresponding to the aforementioned inaccuracies. The training procedure includes parameter randomization and random excitation. That is, the data-driven virtual sensor was trained using data from multiple different powertrain instances, representing roughly the same powertrain. The virtual sensor trained using multiple instances of a simulated powertrain was accurate at estimating rotating speeds and torque of the loaded shaft of multiple simulated test powertrains. The estimates were computed from the rotating speeds and torque at the motor shaft of the powertrain. This research gives excellent grounds for further studies towards simulation-to-reality transfer learning, in which a virtual sensor is trained with simulated data and then applied to a real system.

The paper deals with coupled flexural-torsional vibrations of straight prismatic elastic bars made of a linearly elastic isotropic and homogeneous material. One of the aims is to develop an effective method of modelling vibrations of train rails of cross-sections being mono-symmetric, taking into account warping due to torsion as well as transverse shear deformations. The Librescu-Song 1D model has been appropriately adapted to the above research aims by incorporating all the inertia terms corresponding to the kinematic hypotheses. The finite element(FE) program has been written and its correctness has been verified. The results concerning natural vibrations compare favourably with those predicted by 3D FE modelling using dense meshes. The paper proves that neglecting warping due to torsion leads to omitting several eigen-modes of vibrations, thus showing that the popular Timoshenko-like models are useless for the vibration analysis of bars of mono-symmetric cross sections.