• 文献检索
  • 文档翻译
  • 深度研究
  • 学术资讯
  • Suppr Zotero 插件Zotero 插件
  • 邀请有礼
  • 套餐&价格
  • 历史记录
应用&插件
Suppr Zotero 插件Zotero 插件浏览器插件Mac 客户端Windows 客户端微信小程序
定价
高级版会员购买积分包购买API积分包
服务
文献检索文档翻译深度研究API 文档MCP 服务
关于我们
关于 Suppr公司介绍联系我们用户协议隐私条款
关注我们

Suppr 超能文献

核心技术专利:CN118964589B侵权必究
粤ICP备2023148730 号-1Suppr @ 2026

文献检索

告别复杂PubMed语法,用中文像聊天一样搜索,搜遍4000万医学文献。AI智能推荐,让科研检索更轻松。

立即免费搜索

文件翻译

保留排版,准确专业,支持PDF/Word/PPT等文件格式,支持 12+语言互译。

免费翻译文档

深度研究

AI帮你快速写综述,25分钟生成高质量综述,智能提取关键信息,辅助科研写作。

立即免费体验

用于滚动轴承预测性维护管理的高可靠性多组件微机电系统传感器

Highly Reliable Multicomponent MEMS Sensor for Predictive Maintenance Management of Rolling Bearings.

作者信息

Landi Elia, Prato Andrea, Fort Ada, Mugnaini Marco, Vignoli Valerio, Facello Alessio, Mazzoleni Fabrizio, Murgia Michele, Schiavi Alessandro

机构信息

Department of Information Engineering and Mathematical Sciences, University of Siena, 53100 Siena, Italy.

Division of Applied Metrology and Engineering INRiM, National Institute of Metrological Research, 10135 Turin, Italy.

出版信息

Micromachines (Basel). 2023 Feb 2;14(2):376. doi: 10.3390/mi14020376.

DOI:10.3390/mi14020376
PMID:36838075
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC9962216/
Abstract

In the field of vibration monitoring and control, the use of low-cost multicomponent MEMS-based accelerometer sensors is nowadays increasingly widespread. Such sensors allow implementing lightweight monitoring systems with low management costs, low power consumption and a small size. However, for the monitoring systems to provide trustworthy and meaningful data, the high accuracy and reliability of sensors are essential requirements. Consequently, a metrological approach to the calibration of multi-component accelerometer sensors, including appropriate uncertainty evaluations, are necessary to guarantee traceability and reliability in the frequency domain of data provided, which nowadays is not fully available. In addition, recently developed metrological characterizations at the microscale level allow to provide detailed and accurate quantification of the enhanced technical performance and the responsiveness of these sensors. In this paper, a dynamic calibration procedure is applied to provide the sensitivity parameters of a low-cost, multicomponent MEMS sensor accelerometer prototype (MDUT), designed, developed and realized at the University of Siena, conceived for rolling bearings vibration monitoring in a broad frequency domain (from 10 Hz up to 25 kHz). The calibration and the metrological characterization of the MDUT are carried out by comparison to a reference standard transducer, at the Primary Vibration Laboratory of the National Institute of Metrological Research (INRiM).

摘要

在振动监测与控制领域,如今基于低成本多分量MEMS的加速度计传感器的应用越来越广泛。此类传感器能够实现管理成本低、功耗低且体积小的轻量化监测系统。然而,为使监测系统提供可靠且有意义的数据,传感器的高精度和可靠性是必不可少的要求。因此,采用一种计量方法对多分量加速度计传感器进行校准,包括进行适当的不确定度评估,对于保证所提供数据在频域中的可追溯性和可靠性是必要的,而目前这一点尚未完全实现。此外,最近在微观尺度上开展的计量表征能够对这些传感器增强的技术性能和响应能力进行详细且准确的量化。本文应用一种动态校准程序来提供一种低成本多分量MEMS传感器加速度计原型(被测量装置)的灵敏度参数,该原型由锡耶纳大学设计、开发和实现,旨在用于宽频域(从10赫兹到25千赫兹)的滚动轴承振动监测。在国家计量研究所在(INRiM)的主振动实验室中,通过与参考标准传感器进行比较,对被测量装置进行校准和计量表征。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5e7f/9962216/a51ef51300bb/micromachines-14-00376-g016.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5e7f/9962216/839474b6c730/micromachines-14-00376-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5e7f/9962216/0acefcbd38e4/micromachines-14-00376-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5e7f/9962216/2eca6da53a84/micromachines-14-00376-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5e7f/9962216/14068a99e1ed/micromachines-14-00376-g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5e7f/9962216/0e24b3ffb45f/micromachines-14-00376-g005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5e7f/9962216/2378f91d0b5d/micromachines-14-00376-g006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5e7f/9962216/3ee68bb1fb9c/micromachines-14-00376-g007.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5e7f/9962216/ea4c59080a22/micromachines-14-00376-g008.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5e7f/9962216/1b462ffa12cb/micromachines-14-00376-g009.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5e7f/9962216/962e3f572554/micromachines-14-00376-g010.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5e7f/9962216/71db2620a767/micromachines-14-00376-g011.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5e7f/9962216/261226a726b3/micromachines-14-00376-g012.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5e7f/9962216/c2c1253a699c/micromachines-14-00376-g013.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5e7f/9962216/1e7f821d2862/micromachines-14-00376-g014.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5e7f/9962216/ca04cf8fc5bd/micromachines-14-00376-g015.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5e7f/9962216/a51ef51300bb/micromachines-14-00376-g016.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5e7f/9962216/839474b6c730/micromachines-14-00376-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5e7f/9962216/0acefcbd38e4/micromachines-14-00376-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5e7f/9962216/2eca6da53a84/micromachines-14-00376-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5e7f/9962216/14068a99e1ed/micromachines-14-00376-g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5e7f/9962216/0e24b3ffb45f/micromachines-14-00376-g005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5e7f/9962216/2378f91d0b5d/micromachines-14-00376-g006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5e7f/9962216/3ee68bb1fb9c/micromachines-14-00376-g007.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5e7f/9962216/ea4c59080a22/micromachines-14-00376-g008.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5e7f/9962216/1b462ffa12cb/micromachines-14-00376-g009.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5e7f/9962216/962e3f572554/micromachines-14-00376-g010.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5e7f/9962216/71db2620a767/micromachines-14-00376-g011.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5e7f/9962216/261226a726b3/micromachines-14-00376-g012.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5e7f/9962216/c2c1253a699c/micromachines-14-00376-g013.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5e7f/9962216/1e7f821d2862/micromachines-14-00376-g014.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5e7f/9962216/ca04cf8fc5bd/micromachines-14-00376-g015.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5e7f/9962216/a51ef51300bb/micromachines-14-00376-g016.jpg

相似文献

1
Highly Reliable Multicomponent MEMS Sensor for Predictive Maintenance Management of Rolling Bearings.用于滚动轴承预测性维护管理的高可靠性多组件微机电系统传感器
Micromachines (Basel). 2023 Feb 2;14(2):376. doi: 10.3390/mi14020376.
2
Evaluation of the Diagnostic Sensitivity of Digital Vibration Sensors Based on Capacitive MEMS Accelerometers.基于电容式微机电系统加速度计的数字振动传感器诊断灵敏度评估
Sensors (Basel). 2024 Jul 10;24(14):4463. doi: 10.3390/s24144463.
3
Monitoring and Predictive Maintenance of Centrifugal Pumps Based on Smart Sensors.基于智能传感器的离心泵监测与预知性维护。
Sensors (Basel). 2022 Mar 9;22(6):2106. doi: 10.3390/s22062106.
4
Developing and Testing High-Performance SHM Sensors Mounting Low-Noise MEMS Accelerometers.开发和测试安装低噪声MEMS加速度计的高性能结构健康监测(SHM)传感器。
Sensors (Basel). 2024 Apr 10;24(8):2435. doi: 10.3390/s24082435.
5
Pressure calibration of a digital microelectromechanical system microphone by comparison.通过比较对数字微机电系统麦克风进行压力校准。
J Acoust Soc Am. 2018 Oct;144(4):EL297. doi: 10.1121/1.5059333.
6
Low cost MEMS accelerometer and microphone based condition monitoring sensor, with LoRa and Bluetooth Low Energy radio.基于低成本微机电系统(MEMS)加速度计和麦克风的状态监测传感器,配备LoRa和低功耗蓝牙无线电。
HardwareX. 2024 Mar 26;18:e00525. doi: 10.1016/j.ohx.2024.e00525. eCollection 2024 Jun.
7
Online Monitoring of Sensor Calibration Status to Support Condition-Based Maintenance.在线监测传感器校准状态,支持基于状态的维护。
Sensors (Basel). 2023 Feb 21;23(5):2402. doi: 10.3390/s23052402.
8
High-Performance Piezoelectric-Type MEMS Vibration Sensor Based on LiNbO Single-Crystal Cantilever Beams.基于铌酸锂单晶悬臂梁的高性能压电式微机电系统振动传感器。
Micromachines (Basel). 2022 Feb 19;13(2):329. doi: 10.3390/mi13020329.
9
MEMS Inertial Sensor Calibration Technology: Current Status and Future Trends.微机电系统惯性传感器校准技术:现状与未来趋势
Micromachines (Basel). 2022 May 31;13(6):879. doi: 10.3390/mi13060879.
10
Cost-Effective Data Acquisition Systems for Advanced Structural Health Monitoring.用于先进结构健康监测的经济高效数据采集系统。
Sensors (Basel). 2024 Jun 30;24(13):4269. doi: 10.3390/s24134269.

引用本文的文献

1
Evaluation of the Diagnostic Sensitivity of Digital Vibration Sensors Based on Capacitive MEMS Accelerometers.基于电容式微机电系统加速度计的数字振动传感器诊断灵敏度评估
Sensors (Basel). 2024 Jul 10;24(14):4463. doi: 10.3390/s24144463.
2
A Low Complexity Rolling Bearing Diagnosis Technique Based on Machine Learning and Smart Preprocessing.一种基于机器学习和智能预处理的低复杂度滚动轴承诊断技术
Sensors (Basel). 2023 Aug 30;23(17):7546. doi: 10.3390/s23177546.

本文引用的文献

1
A Review on Rolling Bearing Fault Signal Detection Methods Based on Different Sensors.基于不同传感器的滚动轴承故障信号检测方法综述。
Sensors (Basel). 2022 Oct 30;22(21):8330. doi: 10.3390/s22218330.
2
Evolution and Applications of Recent Sensing Technology for Occupational Risk Assessment: A Rapid Review of the Literature.最新职业风险评估传感技术的发展与应用:文献快速综述。
Sensors (Basel). 2022 Jun 27;22(13):4841. doi: 10.3390/s22134841.
3
Tool Condition Monitoring for High-Performance Machining Systems-A Review.高性能加工系统的刀具状态监测技术综述
Sensors (Basel). 2022 Mar 12;22(6):2206. doi: 10.3390/s22062206.
4
Reduction of calibration uncertainty due to mounting of three-axis accelerometers using the intrinsic properties model.使用本征特性模型安装三轴加速度计以降低校准不确定性。
Metrologia. 2021 Apr 13;58(3). doi: 10.1088/1681-7575/abeccf.
5
A Survey on Data-Driven Predictive Maintenance for the Railway Industry.面向铁路行业的数据驱动预测性维护调查。
Sensors (Basel). 2021 Aug 26;21(17):5739. doi: 10.3390/s21175739.
6
Data-Driven Fault Diagnosis for Electric Drives: A Review.数据驱动的电机驱动故障诊断:综述。
Sensors (Basel). 2021 Jun 10;21(12):4024. doi: 10.3390/s21124024.