Structural Health Monitoring (SHM) is crucial for the development of safe infrastructures. Onboard vibration diagnostics implemented by means of smart embedded sensors is a suitable approach to achieve accurate prediction supported by low-cost systems. Networks of sensors can be installed in isolated infrastructures allowing periodic monitoring even in the absence of stable power sources and connections. To fulfill this goal, the present paper proposes an effective solution based on intelligent extreme edge nodes that can sense and compress vibration data onboard, and extract from it a reduced set of statistical descriptors that serve as input features for a machine learning classifier, hosted by a central aggregating unit. Accordingly, only a small batch of meaningful scalars needs to be outsourced in place of long time series, hence paving the way to a considerable decrement in terms of transmission time and energy expenditure. The proposed approach has been validated using a real-world SHM dataset for the task of damage identification from vibration signals. Results demonstrate that the proposed sensing scheme combining data compression and feature estimation at the sensor level can attain classification scores always above 94%, with a sensor life cycle extension up to 350x and 1510x if compared with compression-only and processing-free implementations, respectively.

Vibration Monitoring in the Compressed Domain with Energy-Efficient Sensor Networks

Zonzini, Federica
Co-primo
;
De Marchi, Luca
Penultimo
;
2023

Abstract

Structural Health Monitoring (SHM) is crucial for the development of safe infrastructures. Onboard vibration diagnostics implemented by means of smart embedded sensors is a suitable approach to achieve accurate prediction supported by low-cost systems. Networks of sensors can be installed in isolated infrastructures allowing periodic monitoring even in the absence of stable power sources and connections. To fulfill this goal, the present paper proposes an effective solution based on intelligent extreme edge nodes that can sense and compress vibration data onboard, and extract from it a reduced set of statistical descriptors that serve as input features for a machine learning classifier, hosted by a central aggregating unit. Accordingly, only a small batch of meaningful scalars needs to be outsourced in place of long time series, hence paving the way to a considerable decrement in terms of transmission time and energy expenditure. The proposed approach has been validated using a real-world SHM dataset for the task of damage identification from vibration signals. Results demonstrate that the proposed sensing scheme combining data compression and feature estimation at the sensor level can attain classification scores always above 94%, with a sensor life cycle extension up to 350x and 1510x if compared with compression-only and processing-free implementations, respectively.
2023
Ragusa, Edoardo; Zonzini, Federica; De Marchi, Luca; Gastaldo, Paolo
File in questo prodotto:
File Dimensione Formato  
ED Vibration_Monitoring_in_the_Compressed.pdf

accesso riservato

Tipo: Versione (PDF) editoriale
Licenza: Licenza per accesso riservato
Dimensione 428.07 kB
Formato Adobe PDF
428.07 kB Adobe PDF   Visualizza/Apri   Contatta l'autore
PP Vibration_Monitoring_in_the_Compressed.pdf

accesso aperto

Tipo: Postprint
Licenza: Licenza per accesso libero gratuito
Dimensione 397 kB
Formato Adobe PDF
397 kB Adobe PDF Visualizza/Apri

I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.

Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11585/937834
Citazioni
  • ???jsp.display-item.citation.pmc??? ND
  • Scopus 0
  • ???jsp.display-item.citation.isi??? 0
social impact