An Energy-Efficient Field-Programmable Gate Array Rapid Implementation of a Structural Health Monitoring System

Maciej Rosół () and Wojciech Kula
Additional contact information
Maciej Rosół: Faculty of Electrical Engineering Automatics Computer Science and Biomedical Engineering, AGH University of Krakow, Avenue Mickiewicza 30, 30-059 Krakow, Poland
Wojciech Kula: Faculty of Electrical Engineering Automatics Computer Science and Biomedical Engineering, AGH University of Krakow, Avenue Mickiewicza 30, 30-059 Krakow, Poland

Energies, 2024, vol. 17, issue 11, 1-15

Abstract: System health monitoring (SHM) of a ball screw laboratory system using an embedded real-time platform based on Field-Programmable Gate Array (FPGA) technology was developed. The ball screw condition assessment algorithms based on machine learning approaches implemented on multiple platforms were compared and evaluated. Studies on electric power consumption during the processing of the proposed structure of a neural network, implementing SHM, were carried out for three hardware platforms: computer, Raspberry Pi 4B, and Kria KV260. It was found that the average electrical power consumed during calculations is the lowest for the Kria platform using the FPGA system. However, the best ratio of the average power consumption to the accuracy of the neural network was obtained for the Raspberry Pi 4B. The concept of an efficient and energy-saving hardware platform that enables monitoring and analysis of the operation of the selected dynamic system was proposed. It allows for easy integration of many software environments (e.g., MATLAB and Python) with the System-on-a-Chip (SoC) platform containing an FPGA and a CPU.

Keywords: health monitoring; energy efficiency; FPGA technology; embedded system; hardware accelerators; neural network (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2024
References: View complete reference list from CitEc
Citations:

Downloads: (external link)
https://www.mdpi.com/1996-1073/17/11/2626/pdf (application/pdf)
https://www.mdpi.com/1996-1073/17/11/2626/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jeners:v:17:y:2024:i:11:p:2626-:d:1404702

Access Statistics for this article

Energies is currently edited by Ms. Agatha Cao

More articles in Energies from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().