Optimisation of nonlinear MEMS electrostatic kinetic energy harvesters to enable self-powered structural health monitoring

Zaghari, Bahareh, Cottone, Francessco, Weddell, Alexander, Basset, Philippe, Lu, Yingxian and Beeby, Stephen (2020) Optimisation of nonlinear MEMS electrostatic kinetic energy harvesters to enable self-powered structural health monitoring. In 10th European Workshop on Structural Health Monitoring (EWSHM 2020).

Abstract

Condition monitoring of gearboxes, a key element of rotating machines, has previously been performed by analysing a shaft vibration data. This recorded vibration signal has distinct dominant frequencies that are stationary. The input signal with multiple dominant frequencies (including harmonics) could excite a kinetic nonlinear energy harvester and provide sufficient power for intelligent sensing. The nonlinear Electrostatic Kinetic Energy Harvesters (e-KEH) proposed in this paper could generate energy under low and high frequency excitation from shaft frequency and the harmonics, respectively. This paper reviews recent developments and challenges in designing MEMS e-KEH for Structural Health Monitoring (SHM), especially for gearboxes in aircraft engine. E-KEHs can have a silicon resonator, which is coupled with elastic silicon beams. Numerical predictive models of MEMS e-KEHs provide a tool to analyse the performance and efficiency of these harvesters. An analytical model of an impact-coupled e-KEH to predict the efficiency at low frequency excitation (under 200 Hz) and mechanical oscillation (under 3g) is presented.

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Contributors

Author: Alexander Weddell

Author: Stephen Beeby

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