Adversarially-Trained Tiny Autoencoders for Near-Sensor Continuous Structural Health Monitoring

Structural Health Monitoring (SHM) systems are increasingly employed in many civil structures such as buildings, tunnels and viaducts. Typical installations consist of sensors that gather information and send it to a central computing unit, which then periodically analyzes the incoming data and produces an assessment of the structure conditions. To avoid the transmission of a huge amount of raw data and reduce latency in the detection of structural anomalies, recent works focus on moving computation on the sensor nodes. This work shows that a small autoencoder, which fits the tiny 2 MB memory of a typical microcontroller used for SHM sensor nodes can achieve very competitive accuracy in detecting structural anomalies as well as vehicle passage on bridges by leveraging adversarial training based on generative adversarial networks (GANs). We improve accuracy over state-of-the-art algorithms in two use-cases on real-standing buildings: i) predicting anomalies on a bridge (+7.4%) and ii) detecting vehicles on a viaduct (2.30×).