A Primer for tinyML Predictive Maintenance: Input and Model Optimisation

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.

A Machine Learning-Oriented Survey on Tiny Machine Learning

“…In [32], the authors investigate techniques used to optimize TinyML-based PdM systems. They describe PdM, and how TinyML can provide an alternative to cloud-based PdM, showing commonly used libraries, hardware, datasets, and models.…”