EMEA 2021 Student Forum
TinyML meets vibration-based Structural Health Monitoring: solving a binary classification problem at the edge
Federica ZONZINI, PhD Student, University of Bologna, Italy
Structural Health Monitoring (SHM) is a trending discipline aiming at assessing the integrity condition of structures throughout their life cycle and in their normal operations. Hence, low-latency, long-term and real-time functionalities are three pillar design criteria to be leveraged while designing a resilient and effective monitoring system.
To this end, the Tiny Machine Learning (TinyML) paradigm has very recently pioneered outstanding solutions capable to optimize both the time and the dimension of the data to be processed and shared among the SHM network. TinyML could indeed bring a radical shift of perspective, moving from cloud-based data analytics, which is usually performed on remote servers in a time and energy consuming manner, to sensor-near data inference, empowered to smart sensors in charge of processing information in a streaming fashion.
Within this scenario, damage diagnosis and prognosis are the two main tasks in which TinyML founds its natural application. Accordingly, the contribution of this work is to present the practical embodiment of TinyML architectures on resource-constrained devices, devoted to the health assessment of structures in dynamic regime. The latter comprise all those application domains which can be thoroughly described by frequency-related quantities, which are conventionally extracted by means of standard spectral analysis techniques.
More in detail, the structural evaluation process was tackled as a binary classification problem. Such an approach yielded to the design of two different neural networks, which require natural frequencies of vibration as inputs and provide the corresponding damage status (i.e. healthy/unhealthy structure) as output. The neural network topology was taken from a standard feedforward Autoassociative Neural Network (ANN). However, due to the high dependency of these frequency parameters from environmental and operational factors (EOF), the neural network models were corrected by including a set of structurally sensitive EOF data (e.g. temperature) as additional inputs to the model. Differently from conventional implementations, the primary aim in pursuing this strategy is to make the network self-adaptative in regressing the EOF-to-frequency relationship without needing any standard regression and/or compensation technique to be performed aside.
The tested ANN models have been initially coded in the Python TensorFlow programming environment; then, their distilled versions, characterized by much a fewer number of hyperparamaters but similar classification performances, have been obtained after conversion to TensorFlow Lite. Finally, the sought models have been ported on the Arduino Nano 33 BLE Sense platform and validated with experimental data from the Z24 bridge use case, reaching an average accuracy and precision of 92% and 91%, respectively. The maximum model size was kept below 10 KB and the maximally measured execution time inferior to 1.5 s.