The impact of Sensing Parameters on Vibration Anomaly Detection Using Tiny DNNs

Smart sensors with extreme-edge data processing capabilities allow to optimize the energy budget of current Structural Health Monitoring (SHM) systems. Moving inference and Deep Neural Networks (DNNs) directly to the sensing unit can bypass the constraints due to data transmission, but it has to be properly balanced against the expenditure due to data sampling, which might easily become the main source of energy consumption when the duty cycling is not chosen appropriately. Finding the optimal sampling time and frequency is a non trivial task because it depends on the peculiarities of the target facility, the surrounding environment, and the highly non-linear behavior of DNNs. This paper presents a fully decentralized scheme for vibration damage detection where each sensor can predict the health status of the structure; a tiny Convolutional Neural Network (CNN) hosted on the node is entitled to autonomously process the measurements and to complete the inference. As a further contribution, an empirical study proves the major role played by the sampling parameters on the performance of the networks. Experiments on the Z24 bridge benchmark show that the small-size CNN can achieve classification scores comparable with state-of-the-art models while i) avoiding data transmission and ii) improving the energy budget if the optimal sensing parameters are chosen. These findings are obtained after deploying the sought CNN model on a wireless accelerometer sensor based on an STM32L496 processor. Results show that, by properly tuning the sampling parameters (length of the acquisition window and sampling frequency), one can reach the best trade-off between classification performances (accuracy above 96%) and energy consumption (less than 40 mJ/h).