A Primer for tinyML Predictive Maintenance: Input and Model Optimisation - Artificial Intelligence Applications and Innovations Access content directly
Conference Papers Year : 2022

A Primer for tinyML Predictive Maintenance: Input and Model Optimisation

Emil Njor
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Jan Madsen
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Xenofon Fafoutis
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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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hal-04668653 , version 1 (07-08-2024)

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Emil Njor, Jan Madsen, Xenofon Fafoutis. A Primer for tinyML Predictive Maintenance: Input and Model Optimisation. 18th IFIP International Conference on Artificial Intelligence Applications and Innovations (AIAI), Jun 2022, Hersonissos, Greece. pp.67-78, ⟨10.1007/978-3-031-08337-2_6⟩. ⟨hal-04668653⟩
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