Self-sensing method for semi-active structural identification by removing piecewise bias from piezoelectric voltage

Hara, Yushin; TANG, Tianyi; Otsuka, Kiyoshi; Makihara, Kanjuro

doi:10.1016/j.sna.2022.113907

Cited by 1 publication

(1 citation statement)

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“…The piezoelectric transducer is assumed to be deformed and polarized in one direction. The constitutive equations of the piezoelectric transducer (Hara, Tang, Otsuka, and Makihara, 2022) are described as…”

Section: Modelingmentioning

confidence: 99%

Energy Saving Structural Health Monitoring Using Semi-Active Identification

Hara,

Tang,

Otsuka

et al. 2023

PHMAP_CONF

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This paper presents a novel approach to achieving system identification of a structure while minimizing energy consumption. The identified structural model can be used for structural health monitoring. In the aerospace environment, energy consumption is strictly regulated. To address this issue, we propose an energy-saving identification method that utilizes piezoelectric semi-active control as an input generation technique. This approach generates control force through electric switch activation, resulting in a smaller amount of energy consumption for input generation than conventional active control. We achieved semi-active input generation suitable for identification by incorporating a novel control strategy. The semi-active control has the disadvantage of limiting the free control of inputs. The identification performance may degrade if the properties of the semi-active input deviate from the desired ones. To address this issue, we also propose a data processing method that extracts a certain input with appropriate properties for identification from the acquired input. We validated the proposed method through numerical simulations and experiments. The results confirmed the feasibility of the semi-active identification method for structural health monitoring. Additionally, we found that the total energy consumption during the 20-second experiment was only 68 mJ to identify the 50 kg structure.

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Section: Modelingmentioning

confidence: 99%