RED: RFID-based Eccentricity Detection for High-speed Rotating Machinery
| Autor: | Meng Jin, Xiaolong Zheng, Yunhao Liu, Yuan He, Yilun Zheng |
|---|---|
| Rok vydání: | 2018 |
| Předmět: |
Computer science
media_common.quotation_subject Real-time computing 0202 electrical engineering electronic engineering information engineering Latency (audio) 020206 networking & telecommunications 020201 artificial intelligence & image processing Sample (statistics) 02 engineering and technology Eccentricity (behavior) Stability (probability) media_common |
| Zdroj: | INFOCOM |
| DOI: | 10.1109/infocom.2018.8485873 |
| Popis: | Eccentricity detection is a crucial issue for highspeed 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 realtime 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. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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