<i>RF-Ear</i>$^+$: A Mechanical Identification and Troubleshooting System Based on Contactless Vibration Sensing
Yuanhao Feng, Youwei Zhang, Panlong Yang, Hao Zhou, Haohua Du, Xiang‐Yang Li
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 <i>RF-Ear<inline-formula><tex-math notation="LaTeX">$^+$</tex-math><alternatives><mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:msup><mml:mrow/><mml:mo>+</mml:mo></mml:msup></mml:math><inline-graphic xlink:href="feng-ieq2-3210150.gif" xmlns:xlink="http://www.w3.org/1999/xlink"/></alternatives></inline-formula></i> , a contactless vibration sensing system using Commercial off-the-shelf (COTS) RFID. <i>RF-Ear<inline-formula><tex-math notation="LaTeX">$^+$</tex-math><alternatives><mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:msup><mml:mrow/><mml:mo>+</mml:mo></mml:msup></mml:math><inline-graphic xlink:href="feng-ieq3-3210150.gif" xmlns:xlink="http://www.w3.org/1999/xlink"/></alternatives></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. <i>RF-Ear<inline-formula><tex-math notation="LaTeX">$^+$</tex-math><alternatives><mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:msup><mml:mrow/><mml:mo>+</mml:mo></mml:msup></mml:math><inline-graphic xlink:href="feng-ieq4-3210150.gif" xmlns:xlink="http://www.w3.org/1999/xlink"/></alternatives></inline-formula></i> can measure the vibration with a frequency up to 987 Hz at a mean error rate of <inline-formula><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, <i>RF-Ear<inline-formula><tex-math notation="LaTeX">$^+$</tex-math><alternatives><mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:msup><mml:mrow/><mml:mo>+</mml:mo></mml:msup></mml:math><inline-graphic xlink:href="feng-ieq6-3210150.gif" xmlns:xlink="http://www.w3.org/1999/xlink"/></alternatives></inline-formula></i> can detect the rotating machinery faults based on the constructed spectrogram, which achieves <inline-formula><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.