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

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 costprohibitive. 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).