Details

Autor(en) / Beteiligte

• Ragusa, Edoardo
• Zonzini, Federica
• Gastaldo, Paolo
• De Marchi, Luca

Titel

Combining Compressed Sensing and Neural Architecture Search for Sensor-Near Vibration Diagnostics

Ist Teil von

• IEEE transactions on industrial informatics, 2024, p.1-11

Ort / Verlag

IEEE

Erscheinungsjahr

2024

Link zum Volltext

Quelle

IEEE Xplore Digital Library

Beschreibungen/Notizen

• Compressed sensing (CS) for sensor-near vibration diagnostics represents a suitable approach for the design of network-efficient structural health monitoring systems. This article presents a solution for vibration analysis based on deep neural networks (DNNs) trained on compressed data. The envisioned maintenance system consists of a network of sensing nodes orchestrated by a very constrained centralizing unit. The latter is equipped with a microcontroller unit (MCU) that predicts the health state using the aggregated information. As a major contribution, the DNN architectures are generated automatically from the data through a procedure inspired by hardware-aware (HW) neural architecture search (NAS), called as HW-NAS-CS, which is uniquely refined with additional constraints that consider both the peculiarities of CS parameters and the limitation of embedded devices. The proposed approach has been validated using two real-world SHM datasets for vibration damage identification and eventually deployed on a low-end computing platform (the STM32L5 MCU). Results demonstrate that DNNs combined with adapted CS schemes can attain classification scores always above 90% even in case of very huge compression levels (higher than 64x): these performances significantly improve the ones attained by state-of-the-art approaches in the field, with the utmost advantage of being portable on embedded devices.