A Self-powered Predictive Maintenance System Based on Piezoelectric Energy Harvesting and TinyML (ISLPED)

Abstract

Nowadays, the Industrial Internet of Things (IIoT) plays a more and more significant role in smart manufacturing. Predictive Maintenance (PdM) is one of the essential applications, recognizing the current status of the machine and preventing disastrous breakdowns. End-point sensors for such monitoring systems are powered mainly by batteries. As IIoT grows, constantly replacing batteries across thousands of devices is cost-prohibitive. In addition, tremendous original sensing data are wirelessly transmitted to the server for data analysis, causing colossal energy consumption. In this paper, we propose the first self-powered on-device PdM system based on piezoelectric energy harvesting and tiny machine learning (TinyML). A trained TinyML model is deployed on the low-cost microcontroller (MCU) for on-device inferring; only the diagnosis result is transmitted. With an emphasis on ultra-low-power demands, a piezoelectric energy harvester is utilized as an energy source and self-powered sensor (SPS) simultaneously. The energy-aware circuit provides reconfigurable on/off threshold voltages for efficient and robust intermittent operation. The balance between energy supply and demand in the battery-free system has been achieved by a handy design. A rich SPS dataset has been collected in a simulated vibration environment and analyzed by five well-known machine-learning models. Random forest stands out given ultra-small data length and sampling rate with accuracy up to 99% for four-class similar vibration diagnosis. Lab test validates the feasibility and performance. As a cyber-electro-mechanical co-design, the system provides a promising solution to the ubiquitous artificial intelligence of things (AIoT).