A Holistic Review of the TinyML Stack for Predictive Maintenance

Abstract

Downtime caused by failing equipment can be extremely costly for organizations. Predictive Maintenance (PdM), which uses data to predict when maintenance should be conducted, is an essential tool for increasing safety, maximizing uptime and minimizing costs. Contempoary PdM systems primarily have sensors collect information about the equipment under observation. This information is afterwards transmitted off the device for processing at a high-performance computer system. While this can allow high-quality predictions, it also imposes barriers that keep some organisations from adopting PdM. For example, some applications prevent data transmission off sensor devices due to regulatory or infrastructure limitations. Being able to process the collected information right at the sensor device is, therefore, desirable in many sectors - something that recent progress in the field of TinyML promises to deliver. This paper investigates the intersection between PdM and TinyML and explores how TinyML can enable many new PdM applications. We consider a holistic view of TinyML-based PdM, focusing on the full stack of Machine Learning (ML) models, hardware, toolchains, data and PdM applications. Our main findings are that each part of the TinyML stack has received varying degrees of attention. In particular, ML models and their optimisations have seen a lot of attention, while data optimisations and TinyML datasets lack contributions. Furthermore, most TinyML research focuses on image and audio classification, with little attention paid to other application areas such as PdM. Based on our observations, we suggest promising avenues of future research to scale and improve the application of TinyML to PdM.

Keywords

• Embedded AI
• embedded machine learning
• optimization
• predictive maintenance
• resource-constrained systems
• TinyML