Energy Saving Structural Health Monitoring Using Semi-Active Identification

##plugins.themes.bootstrap3.article.main##

##plugins.themes.bootstrap3.article.sidebar##

Published Sep 4, 2023
Yushin Hara Tianyi Tang Keisuke Otsuka Kanjuro Makihara

Abstract

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.  

Abstract 78 | PDF Downloads 81

##plugins.themes.bootstrap3.article.details##

Keywords

Structural health monitoring, Smart structure, Semi-Active control, Piezoelectric

References
Deraemaeker, A., Reynders, E., De Roeck, G., Kullaa, J. (2008). Vibration-based structural health monitoring using output-only measurements under changing environment. Mechanical Systems and Signal Processing, 22(1), 34–56. doi: 10.1016/j.ymssp.2007 .07.004

Hara, Y., Otsuka, K., Makihara, K. (2023a). Lowenergy-consumption structural identification with switching piezoelectric semi-active input. Mechanical Systems and Signal Processing, 187, 109914. doi: 10.1016/j.ymssp.2022.109914

Hara, Y., Tang, T., Otsuka, K., Makihara, K. (2022). Selfsensing method for semi-active structural identification by removing piecewise bias from piezoelectric voltage. Sensors and Actuators A: Physical, 347, 113907. doi: 10.1016/j.sna.2022.113907

Hara, Y., Tang, T., Otsuka, K., Makihara, K. (2023b). Strategy for performance improvement in piezoelectric semi-active structural system identification by excluding switching failures using pseudo-state feedback. Mechanical Systems and Signal Processing, 187, 109906. doi: 10.1016/j.ymssp.2022 .109906

Le, M. Q., Capsal, J. F., Lallart, M., Hebrard, Y., Van Der Ham, A., Reffe, N., . . . Cottinet, P. J. (2015). Review on energy harvesting for structural health monitoring in aeronautical applications. Progress in Aerospace Sciences, 79, 147–157. doi: 10.1016/ j.paerosci.2015.10.001

Richard, C., Guyomar, D., Audigier, D., Ching, G. (1999). Semi-passive damping using continuous switching of a piezoelectric device. In Smart structures and materials 1999: Passive damping and isolation. doi: 10.1117/12.349773

Tipaldi, M., Bruenjes, B. (2014). Spacecraft health monitoring and management systems. In 2014 ieee metrology for aerospace (metroaerospace) (pp. 68– 72). doi: 10.1109/MetroAeroSpace.2014.6865896

Verhaegen, M., Dewilde, P. (1992). Subspace model identification Part 2. Analysis of the elementary outputerror state-space model identification algorithm. International Journal of Control, 56(5), 1211–1241. doi: 10.1080/00207179208934364
Section
Regular Session Papers