Romeo: Fault Detection of Rotating Machinery via Fine-Grained mmWave Velocity Signature

IF 7.7 2区 计算机科学 Q1 COMPUTER SCIENCE, INFORMATION SYSTEMS
Yanni Yang;Pengfei Hu;Jun Luo;Zhenlin An;Jiannong Cao;Dongxiao Yu;Xiuzhen Cheng
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引用次数: 0

Abstract

Real-time velocity monitoring is pivotal for fault detection of rotating machinery. However, existing methods rely on either troublesome deployments of optical encoders and IMU sensors or various tachometers delivering coarse-grained velocity measurements insufficient for fault detection. To overcome these limitations, we propose Romeo as the first work to exploit the mmWave radar for ro tating m achinery fault detection by extracting a fine-grained v e l o city signature. Though mmWave radars should capture instant rotation information with their claimed high sensitivity and sampling rate, direct adoption entails significant efforts for high-precision velocity measurement per radar to handle; particularly, exhausted system calibration and noise interference. To this end, we first develop a phase-velocity model to characterize the relationship between the mmWave signal phase and the fine-grained angular velocity. We then explore the geometric properties of specific positions in the rotation trajectory to precisely calibrate the rotation sensing model, leading to an iterative algorithm for accurate angular velocity measurement. Finally, we propose a simple yet effective fault detection algorithm by extracting a unique velocity signature. Our extensive experiments show Romeo achieves a median error of 0.4 $^\circ$ /s for fine-grained angular speed measurement, outperforming SOTA solutions with over ×16 angular speed granularity and ×7 measurement precision.
罗密欧:基于细粒度毫米波速度特征的旋转机械故障检测
转速实时监测是旋转机械故障检测的关键。然而,现有的方法要么依赖于光学编码器和IMU传感器的麻烦部署,要么依赖于各种转速计,这些转速计提供的粗粒度速度测量不足以进行故障检测。为了克服这些限制,我们提出Romeo作为第一个利用毫米波雷达通过提取细粒度速度特征来进行旋转机械故障检测的工作。虽然毫米波雷达应该以其声称的高灵敏度和采样率捕获即时旋转信息,但直接采用需要为每台雷达进行高精度速度测量付出巨大努力;特别是排除了系统标定和噪声干扰。为此,我们首先建立了一个相速度模型来表征毫米波信号相位与细粒度角速度之间的关系。然后,我们探索旋转轨迹中特定位置的几何特性,以精确校准旋转传感模型,从而实现精确角速度测量的迭代算法。最后,我们提出了一种简单而有效的故障检测算法,即提取唯一的速度特征。我们的大量实验表明,Romeo在细粒度角速度测量中实现了0.4$^\circ$/s的中位数误差,优于具有×16以上角速度粒度和×7测量精度的SOTA解决方案。
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来源期刊
IEEE Transactions on Mobile Computing
IEEE Transactions on Mobile Computing 工程技术-电信学
CiteScore
12.90
自引率
2.50%
发文量
403
审稿时长
6.6 months
期刊介绍: IEEE Transactions on Mobile Computing addresses key technical issues related to various aspects of mobile computing. This includes (a) architectures, (b) support services, (c) algorithm/protocol design and analysis, (d) mobile environments, (e) mobile communication systems, (f) applications, and (g) emerging technologies. Topics of interest span a wide range, covering aspects like mobile networks and hosts, mobility management, multimedia, operating system support, power management, online and mobile environments, security, scalability, reliability, and emerging technologies such as wearable computers, body area networks, and wireless sensor networks. The journal serves as a comprehensive platform for advancements in mobile computing research.
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