RF-Ear$^+$: A Mechanical Identification and Troubleshooting System Based on Contactless Vibration Sensing

Abstract

Mechanical vibration monitoring plays a critical role in today's industrial Internet of Things (IoT) applications. Existing invasive solutions usually directly attach sensors to the target, which may affect the operations of delicate devices. Non-invasive video-based approaches incur poor performance in low light conditions, and laser-based ones have difficulties to monitor multiple objects simultaneously. In this work, we propose <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">RF-Ear<inline-formula><tex-math notation="LaTeX">$^+$</tex-math></inline-formula></i> , a contactless vibration sensing system using Commercial off-the-shelf (COTS) RFID. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">RF-Ear<inline-formula><tex-math notation="LaTeX">$^+$</tex-math></inline-formula></i> could accurately monitor the mechanical vibrations of multiple devices using a single tag: it can clearly tell which object is vibrating at what frequency without attaching tags on any device. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">RF-Ear<inline-formula><tex-math notation="LaTeX">$^+$</tex-math></inline-formula></i> can measure the vibration with a frequency up to 987Hz at a mean error rate of <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$0.4\%$</tex-math></inline-formula> . We further employ each device's unique vibration fingerprint to identify and differentiate devices of exactly the same model. What's more, <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">RF-Ear<inline-formula><tex-math notation="LaTeX">$^+$</tex-math></inline-formula></i> can detect the rotating machinery faults based on the constructed spectrogram, which achieves <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$98\%$</tex-math></inline-formula> accuracy on 6 types of states. To improve the computation efficiency, we optimize the input of model in both time and frequency domains, and thus enable deployment on low-cost edge devices successfully. Comprehensive experiments conducted in lab and wild demonstrate the effectiveness of our system.