Smart devices for structural health monitoring provide edge computing capabilities to reduce wireless transmission and, thus, power consumption. Although effective algorithms have been proposed in the last few decades, traditional microcontrollers require heavy data flow between the memory and the central processing unit that involves a considerable fraction of the total energy consumption. Phase change memory has recently emerged as an attractive solution in the field of resistive non-volatile memory for analog in-memory computing, which is a valid approach to avoid data being conveyed among distinct elaboration units. However, it has never been envisaged in structural health monitoring applications. As this technology is still in an embryonic state, several challenges related to nonlinearities and nonidealities of the memory elements and the energy expenditure related to the memory reprogramming process may undermine its usage. In this paper, the application of a novel identification approach for civil infrastructures is investigated using phase change memories. The main computational core of the presented algorithm, consisting of 1-dimensional convolutions, is particularly suitable for implementations involving analog in-memory computing, thus showing the great potential of this technology for structural health monitoring applications. The test unit is an embedded phase change memory provided by STMicroelectronics and designed in 90-nm smart power bipolar complementary metal-oxide-semiconductor (CMOS)-double-diffused metal-oxide-semiconductor (DMOS) technology with a Ge-rich Ge-Sb-Te alloy for automotive applications. Experimental results obtained for a viaduct of an Italian motorway support the efficacy of the method. Moreover, the influence of nonidealities on the outcomes of damage identification based on both dynamic and quasi-static structural parameters is examined.

Phase Change Memories in Smart Sensing Solutions for Structural Health Monitoring

Said Quqa
Primo
;
Alessio Antolini;Eleonora Franchi Scarselli;Antonio Gnudi;Andrea Lico;Luca Landi;Pier Paolo Diotallevi
2022

Abstract

Smart devices for structural health monitoring provide edge computing capabilities to reduce wireless transmission and, thus, power consumption. Although effective algorithms have been proposed in the last few decades, traditional microcontrollers require heavy data flow between the memory and the central processing unit that involves a considerable fraction of the total energy consumption. Phase change memory has recently emerged as an attractive solution in the field of resistive non-volatile memory for analog in-memory computing, which is a valid approach to avoid data being conveyed among distinct elaboration units. However, it has never been envisaged in structural health monitoring applications. As this technology is still in an embryonic state, several challenges related to nonlinearities and nonidealities of the memory elements and the energy expenditure related to the memory reprogramming process may undermine its usage. In this paper, the application of a novel identification approach for civil infrastructures is investigated using phase change memories. The main computational core of the presented algorithm, consisting of 1-dimensional convolutions, is particularly suitable for implementations involving analog in-memory computing, thus showing the great potential of this technology for structural health monitoring applications. The test unit is an embedded phase change memory provided by STMicroelectronics and designed in 90-nm smart power bipolar complementary metal-oxide-semiconductor (CMOS)-double-diffused metal-oxide-semiconductor (DMOS) technology with a Ge-rich Ge-Sb-Te alloy for automotive applications. Experimental results obtained for a viaduct of an Italian motorway support the efficacy of the method. Moreover, the influence of nonidealities on the outcomes of damage identification based on both dynamic and quasi-static structural parameters is examined.
Said Quqa, Alessio Antolini, Eleonora Franchi Scarselli, Antonio Gnudi, Andrea Lico, Marcella Carissimi, Marco Pasotti, Roberto Canegallo, Luca Landi, Pier Paolo Diotallevi
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11585/882025
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