Compression–Accuracy Co-Optimization Through Hardware-Aware Neural Architecture Search for Vibration Damage Detection

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Abstract
Internet-of-Things (IoT) is a key enabler for the transition to the Automatic Structural Health Monitoring (ASHM) of technical facilities, thanks to the seamless flow of data from a multitude of always connected devices. Current IoT-ASHM installations, however, face the double challenge to ensure high accuracy while meeting the requirement of minimal energy consumption. The paper tackles these issues from a deep-learning perspective, and describes an IoT-enabled monitoring approach based on a distributed end-to-end deep neural network (DNN). The architecture supports both data compression and damage detection. A low-end microcontroller hosts a specific local DNN; a hardware-aware neural-architecture search strategy rules network optimization, in order to satisfy the resource constraints set by low-end computing devices. The features extracted from data feed an aggregating unit, which includes a stacked global classification layer for full-scale damage detection. After proper quantization, the designed models are eventually deployed on a wireless accelerometer sensor. Finally, a cost-benefit analysis evaluates the system’s impact on the sensor energy autonomy. Experiments on a well-known dataset proved that the proposed solution could achieve state-of-the-art classification scores (all metrics above 98.4%) with a minimal transmission cost (less than 53 B on average); as compared with conventional approaches, the described strategy yielded a reduction of three orders of magnitude in energy consumption.
Funding Information
  • European Commission ("ALMArie CURIE 2022 - Linea SUpER finanziata a val, ?FAIR? project ? Spoke 8 ?Pervasive AI,?)

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