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The continued advancements in Internet of Things (IoT) and Machine Learning (ML) technologies have led to their adoption in various domains including in industries for predictive maintenance among other applications. Given the resource constraints of IoT devices, they cannot process the resource-intensive ML algorithms hence data collected by the devices are first sent to the cloud where the algorithms are hosted for processing and inference with the results being sent back to the devices for action and/or notifications. The need to transmit data to the cloud for processing leads to increased costs, energy consumption, and high latencies affecting the implementation of the solution. Interestingly with Tiny Machine Learning (TinyML), it is possible to develop algorithms enabling edge inference on resource-constrained devices. From existing review papers, the researchers were not able to find, a comprehensive review with a focus on this area showing the need for a targeted review that can shed light on how TinyML can be tailored for predictive maintenance tasks in industries. This study therefore presents a systematic literature review of the application of TinyML in predictive maintenance in industrial settings. TinyML overview and its benefits are presented, a TinyML process flow is proposed and various use cases and their classifications have been presented. Through this exploration, the study shows the critical need for TinyML-driven solutions in predictive maintenance, identifies the existing challenges, and proposes a roadmap for future research.
Ooko et al. (Fri,) studied this question.