{"title":"Romeo: Fault Detection of Rotating Machinery via Fine-Grained mmWave Velocity Signature","authors":"Yanni Yang;Pengfei Hu;Jun Luo;Zhenlin An;Jiannong Cao;Dongxiao Yu;Xiuzhen Cheng","doi":"10.1109/TMC.2024.3463955","DOIUrl":null,"url":null,"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 \n<small>Romeo</small>\n as the first work to exploit the mmWave radar for \n<u>ro</u>\ntating \n<u>m</u>\nachinery fault detection by extracting a fine-grained v\n<u>e</u>\nl\n<u>o</u>\ncity 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 \n<small>Romeo</small>\n achieves a median error of 0.4\n<inline-formula><tex-math>$^\\circ$</tex-math></inline-formula>\n/s for fine-grained angular speed measurement, outperforming SOTA solutions with over ×16 angular speed granularity and ×7 measurement precision.","PeriodicalId":50389,"journal":{"name":"IEEE Transactions on Mobile Computing","volume":"24 1","pages":"227-242"},"PeriodicalIF":7.7000,"publicationDate":"2024-09-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"IEEE Transactions on Mobile Computing","FirstCategoryId":"94","ListUrlMain":"https://ieeexplore.ieee.org/document/10684128/","RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"COMPUTER SCIENCE, INFORMATION SYSTEMS","Score":null,"Total":0}
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.
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.