Abstract
In this paper, we investigate techniques used to optimise tinyML based Predictive Maintenance (PdM). We first describe PdM and tinyML and how they can provide an alternative to cloud-based PdM. We present the background behind deploying PdM using tinyML, including commonly used libraries, hardware, datasets and models. Furthermore, we show known techniques for optimizing tinyML models. We argue that an optimisation of the entire tinyML pipeline, not just the actual models, is required to deploy tinyML based PdM in an industrial setting. To provide an example, we create a tinyML model and provide early results of optimising the input given to the model.
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This work is supported by the Innovation Fund Denmark for the project DIREC (9142-00001B).
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The source code used for the experiments is publicly accessible on GitHub: https://github.com/Ekhao/ToyADMOSTinyAutoencoder.
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Njor, E., Madsen, J., Fafoutis, X. (2022). A Primer for tinyML Predictive Maintenance: Input and Model Optimisation. In: Maglogiannis, I., Iliadis, L., Macintyre, J., Cortez, P. (eds) Artificial Intelligence Applications and Innovations. AIAI 2022. IFIP Advances in Information and Communication Technology, vol 647. Springer, Cham. https://doi.org/10.1007/978-3-031-08337-2_6
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