|Yilun Zheng||Tsinghua University, P.R. China|
|Yuan He||Tsinghua University, P.R. China|
|Meng Jin||Northwest University, P.R. China|
|Xiaolong Zheng||Tsinghua University, P.R. China|
|Yunhao Liu||Tsinghua University & The Hong Kong University of Science and Technology, P.R. China|
Eccentricity detection is a crucial issue for high-speed rotating machinery, which concerns the stability and safety of the machinery. Conventional techniques in industry for eccentricity detection are mainly based on measuring certain physical indicators, which are costly and hard to deploy. In this paper, we propose RED, a non-intrusive, low-cost, and real-time RFID-based eccentricity detection approach. Differing from the existing RFID-based sensing approaches, RED utilizes the temporal and phase distributions of tag readings as effective features for eccentricity detection. RED includes a Markov chain based model called RUM, which only needs a few sample readings from the tag to make a highly accurate and precise judgement. We implement RED with commercial-of-the-shelf RFID reader and tags, and evaluate its performance across various scenarios. The overall accuracy is 93.59% and the detection latency is 0.68 seconds in average.