A Primer for tinyML Predictive Maintenance: Input and Model Optimisation

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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 optimising 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.
Original languageEnglish
Title of host publicationProceedings of 18th International Conference on Artificial Intelligence Applications and Innovations
Volume647
Publication date2022
Pages67-78
ISBN (Print)978-3-031-08336-5
ISBN (Electronic)978-3-031-08337-2
DOIs
Publication statusPublished - 2022
Event18th International Conference on Artificial Intelligence Applications and Innovations - Aldermar Knossos Royal, Crete, Greece
Duration: 17 Jun 202220 Jun 2022
Conference number: 18
https://ifipaiai.org/2022/

Conference

Conference18th International Conference on Artificial Intelligence Applications and Innovations
Number18
LocationAldermar Knossos Royal
Country/TerritoryGreece
CityCrete
Period17/06/202220/06/2022
Internet address

Keywords

  • tinyML
  • Predictive maintenance
  • Optimisation
  • Embedded machine learning
  • Resource-Constrained Systems

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