Multi-Vib: Precise Multi-point Vibration Monitoring Using mmWave Radar

Human Pose & Activity RecognitionBiosensors & Physiological MonitoringSoftware Engineers & DevelopersAI/ML Researchers & EngineersIndustrial Automation Engineers

"Vibration measurement is vital for fault diagnosis of structures (e.g., machines and civil structures). Different structure components undergo distinct vibration patterns, which jointly determine the structure's health condition, thus demanding simultaneous multi-point vibration monitoring. Existing solutions deploy multiple accelerometers along with their power supplies or laser vibrometers on the monitored object to measure multi-point vibration, which is inconvenient and costly. Cameras provide a less expensive solution while heavily relying on good lighting conditions. To overcome these limitations, we propose a cost-effective and passive system, called Multi-Vib, for precise multi-point vibration monitoring. Multi-Vib is implemented using a single mmWave radar to remotely and separately sense the vibration displacement of multiple points via signal reflection. However, simultaneously detecting and monitoring multiple points on a single object is a daunting task. This is because most radar signals are scattered away from vibration points due to their tilted locations and shapes by nature, causing an extremely weak reflected signal to the radar. To solve this issue, we dedicatedly design a physical marker placed on the target point, which can force the direction of the reflected signal towards the radar and significantly increase the reflected signal strength. Another practical issue is that the reflected signal from each point endures interferences and noises from the surroundings. Thus, we develop a series of effective signal processing methods to denoise the signal for accurate vibration frequency and displacement estimation. Extensive experimental results show that the average errors in multi-point vibration frequency and displacement estimation are around 0.16Hz and 14μm, respectively. https://dl.acm.org/doi/10.1145/3569496"

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https://hci.top/en/papers/ubicomp/128411/2023

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Source
UbiComp
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Year
2023
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5 authors
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Human Pose & Activity Recognition, Biosensors & Physiological Monitoring
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Software Engineers & Developers, AI/ML Researchers & Engineers, Industrial Automation Engineers
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Abstract only
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