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Nonlinear free vibration and damping of a clamped–clamped composite beam containing shape memory alloy wires with different prestrains embedded in the midsurface are investigated. The constitutive relations of shape memory alloy are considered in the large deflection response of the elastic Euler–Bernoulli beam, and Hamilton’s principle is used to derive differential equation of the beam motion with extensible midplane. Considering midplane stretching due to tension in the shape memory alloy wires provides an ability to model damping in a nonlinear analysis although there is no distinct damping expression in the governing equation of motion. The results show a gradual decrease in the free vibration amplitude of the beam until it reaches a fully elastic response while this cannot be seen in linear models. Free vibrations of the beam are investigated, and time response, phase diagram, state of stress and strain in the wires, and variations of loss factor are studied. The effect of prestrain in the shape memory alloy wires on the final stable vibration amplitude and loss factor in free vibrations of the beam is also investigated.

In this paper, an artificial neural network system is designed and employed for fault prediction of rotating machinery systems. Multi-layer feedforward networks, constituted of non-linear neurons, have been employed. A normalization scheme is implemented on the input and output vectors. The performance of the expert structure is optimized to encounter input data with different intensities and non-regular data. More than 40 rotating machinery faults are introduced into the algorithm. To train the network, the data in the vibration identification chart consisting of vibration signals of common rotating machinery faults are used. Computer software is developed to detect machinery faults by using the above techniques and is validated for fault detection of different machinery systems. It is found that the designed network is capable of identifing unknown faults in rotating machinery. The effectiveness of the proposed neural network algorithm is displayed by several tests.

This article presents theoretical and experimental investigations of a vibration-based energy harvester which is suitable to extract energy from low-frequency, high-amplitude environments. The proposed device consists of a rotating proof mass and eight piezoelectric bimorph beams. A magnet, mounted on the rotating pendulum, actuates the tips of the piezoelectric beams due to magnetic interaction. The free oscillations of the piezoelectric beam generate energy each time the pendulum passes over the beams. Based on energy principles, the nonlinear ordinary differential equations governing the electromechanical behavior of the system are derived and solved numerically. Using the proposed model, the effects of angular velocity of the rotating mass on the generated voltage are investigated. It is shown that the harvester with a relatively high angular velocity generates more voltage than the one with a low angular velocity. The performance of the proposed device in the attractive and the repulsive cases is compared to each other and it is concluded that the generated voltage of the attractive case is more than that of the repulsive case. The overall generated power of the harvester under harmonic external excitations is investigated for various amplitudes and frequencies. Using theoretical model, it is shown that the proposed device can be used for harvesting the out-of-plane vibrational energy. A model of the system has been devised and tested. The obtained experimental results show good qualitative agreements with the theoretical ones.

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