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Abstract: Power harvesting techniques that convert vibration energy into electrical energy through piezoelectric transducers show strong potential for powering smart wireless sensor devices in applications of structural health monitoring. This paper presents an analytical model of the dynamic behavior of an electromechanical piezoelectric bimorph cantilever harvester connected with an AC–DC circuit based on the Euler–Bernoulli beam theory and Hamiltonian theorem. A new cantilevered piezoelectric bimorph structure is proposed in which the plug-type connection between support layer and tip-mass ensures that the gravity center of the tip-mass is collinear with the gravity center of the beam so that the brittle fracture of piezoelectric layers can also be avoided while vibrating with large amplitude. The tip-mass is equated by the inertial force and inertial moment acting at the end of the piezoelectric bimorph beam based on D''Alembert''s principle. An AC–DC converting circuit soldered with the piezoelectric elements is also taken into account. A completely new analytic expression of the global behavior of the electromechanical piezoelectric bimorph harvesting system with AC–DC circuit under input base transverse excitation is derived. Moreover, an experimental energy harvester is fabricated and the theoretical analysis and experimental results of the piezoelectric harvester under the input base transverse displacement excitation are validated by using measurements of the absolute tip displacement, electric voltage response, electric current response and electric power harvesting. [Copyright &y& Elsevier]

Abstract: Animal forage plays an important role in agriculture. To maintain the silage quality during storage, preserving it from decomposition by wrapping the harvested crop with plastic stretch film is crucial. Any failure in the preserving process permits the infusion of oxygen, which allows the growth of undesirable aerobic microorganisms and silage decomposition. To study the state of the silage, temperature inside the stack could be used as indicator of decomposition. However, no previous study has developed a model that can detect decomposition based on abnormal temperature variations inside a silage stack. The first objective of this paper was to develop a structured nonlinear model to estimate the dynamics of temperature variations measured by a wireless sensor network-based monitoring system (nRF95E) inside a silage bale and stack prior to the decomposition process. The requirement of a robust nonlinear model is that sensor nonlinearities that are inherent in nature are considered. The results showed that a Hammerstein-Wiener (HW) model with sigmoid network performed the best in terms of percentage of fit (91.70%) between the measured and simulated output. The second objective was to estimate the length of time required to detect the decomposition process after the airtight seal was punctured using the model. The error between the predicted model output and the sensor readings exceeded a preset threshold interval 17 days after the cover film was punctured and 9 days after the decomposition process was detected due to the odour released. [Copyright &y& Elsevier]