• 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

基于边缘计算技术的机器状态监测系统

Machine Condition Monitoring System Based on Edge Computing Technology.

作者信息

Halenar Igor, Halenarova Lenka, Tanuska Pavol, Vazan Pavel

机构信息

Institute of Applied Informatics, Automation and Mechatronics, Faculty of Materials Science and Technology in Trnava, Slovak University of Technology in Bratislava, 812 43 Bratislava, Slovakia.

出版信息

Sensors (Basel). 2024 Dec 31;25(1):180. doi: 10.3390/s25010180.

DOI:10.3390/s25010180
PMID:39796971
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC11723020/
Abstract

The core of this publication is the design of a system for evaluating the condition of production equipment and machines by monitoring selected parameters of the production process with an additional sensor subsystem. The main positive of the design is the processing of data from the sensor layer using artificial intelligence (AI) and expert systems (ESs) with the use of edge computing (EC). Sensor information is processed directly at the sensor level on the monitored equipment, and the results of the individual subsystems are stored in the form of triggers in a database for use in the predictive maintenance process. The whole solution includes the design of suitable sensors and of the implementation of the sensor layer, the description of data processing algorithms, the design on the communication infrastructure for the whole system, and tests in the form of experimental operation of the device in laboratory conditions. The solution includes the visualisation of the production system status for the operator using an interactive online map.

摘要

本出版物的核心是设计一个系统，通过使用额外的传感器子系统监测生产过程的选定参数，来评估生产设备和机器的状况。该设计的主要优点是利用人工智能（AI）和专家系统（ES）以及边缘计算（EC）对来自传感器层的数据进行处理。传感器信息在被监测设备的传感器层面直接进行处理，各个子系统的结果以触发器的形式存储在数据库中，以供预测性维护过程使用。整个解决方案包括合适传感器的设计和传感器层的实施、数据处理算法的描述、整个系统通信基础设施的设计，以及在实验室条件下以设备实验运行的形式进行测试。该解决方案包括使用交互式在线地图为操作员可视化生产系统状态。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1d13/11723020/1c9468c13eae/sensors-25-00180-g016.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1d13/11723020/232863e70554/sensors-25-00180-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1d13/11723020/09253ea06c1d/sensors-25-00180-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1d13/11723020/4063ed3bb3a8/sensors-25-00180-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1d13/11723020/7213486cf623/sensors-25-00180-g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1d13/11723020/cc88ccf9f687/sensors-25-00180-g005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1d13/11723020/50691b96b4ad/sensors-25-00180-g006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1d13/11723020/9d44465e3636/sensors-25-00180-g007.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1d13/11723020/00061310e752/sensors-25-00180-g008.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1d13/11723020/ddc4c6297277/sensors-25-00180-g009.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1d13/11723020/db18effbc0f0/sensors-25-00180-g010.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1d13/11723020/034d301e81fa/sensors-25-00180-g011.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1d13/11723020/0c7b978fb3af/sensors-25-00180-g012.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1d13/11723020/4a0bf93a2202/sensors-25-00180-g013.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1d13/11723020/135673bb17b4/sensors-25-00180-g014.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1d13/11723020/43ec05fea7e0/sensors-25-00180-g015.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1d13/11723020/1c9468c13eae/sensors-25-00180-g016.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1d13/11723020/232863e70554/sensors-25-00180-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1d13/11723020/09253ea06c1d/sensors-25-00180-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1d13/11723020/4063ed3bb3a8/sensors-25-00180-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1d13/11723020/7213486cf623/sensors-25-00180-g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1d13/11723020/cc88ccf9f687/sensors-25-00180-g005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1d13/11723020/50691b96b4ad/sensors-25-00180-g006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1d13/11723020/9d44465e3636/sensors-25-00180-g007.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1d13/11723020/00061310e752/sensors-25-00180-g008.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1d13/11723020/ddc4c6297277/sensors-25-00180-g009.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1d13/11723020/db18effbc0f0/sensors-25-00180-g010.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1d13/11723020/034d301e81fa/sensors-25-00180-g011.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1d13/11723020/0c7b978fb3af/sensors-25-00180-g012.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1d13/11723020/4a0bf93a2202/sensors-25-00180-g013.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1d13/11723020/135673bb17b4/sensors-25-00180-g014.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1d13/11723020/43ec05fea7e0/sensors-25-00180-g015.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1d13/11723020/1c9468c13eae/sensors-25-00180-g016.jpg

相似文献

1
Machine Condition Monitoring System Based on Edge Computing Technology.基于边缘计算技术的机器状态监测系统
Sensors (Basel). 2024 Dec 31;25(1):180. doi: 10.3390/s25010180.
2
[Standard technical specifications for methacholine chloride (Methacholine) bronchial challenge test (2023)].[氯化乙酰甲胆碱支气管激发试验标准技术规范（2023年）]
Zhonghua Jie He He Hu Xi Za Zhi. 2024 Feb 12;47(2):101-119. doi: 10.3760/cma.j.cn112147-20231019-00247.
3
Edge-Computing and Machine-Learning-Based Framework for Software Sensor Development.基于边缘计算和机器学习的软件传感器开发框架。
Sensors (Basel). 2022 Jun 3;22(11):4268. doi: 10.3390/s22114268.
4
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.
5
An Industrial Digitalization Platform for Condition Monitoring and Predictive Maintenance of Pumping Equipment.一种用于泵设备状态监测与预测性维护的工业数字化平台。
Sensors (Basel). 2019 Aug 31;19(17):3781. doi: 10.3390/s19173781.
6
From Sensors to Data Intelligence: Leveraging IoT, Cloud, and Edge Computing with AI.从传感器到数据智能：借助人工智能利用物联网、云和边缘计算
Sensors (Basel). 2025 Mar 12;25(6):1763. doi: 10.3390/s25061763.
7
Real-Time Energy Data Acquisition, Anomaly Detection, and Monitoring System: Implementation of a Secured, Robust, and Integrated Global IIoT Infrastructure with Edge and Cloud AI.实时能源数据采集、异常检测和监测系统：实现安全、稳健和集成的全球工业物联网基础设施，具有边缘和云人工智能。
Sensors (Basel). 2022 Nov 20;22(22):8980. doi: 10.3390/s22228980.
8
Real-Time Fault Detection and Condition Monitoring for Industrial Autonomous Transfer Vehicles Utilizing Edge Artificial Intelligence.利用边缘人工智能实现工业自主运输车辆的实时故障检测和状态监测。
Sensors (Basel). 2022 Apr 22;22(9):3208. doi: 10.3390/s22093208.
9
Blockchain and K-Means Algorithm for Edge AI Computing.区块链和 K-Means 算法在边缘人工智能计算中的应用。
Comput Intell Neurosci. 2022 May 29;2022:1153208. doi: 10.1155/2022/1153208. eCollection 2022.
10
TIP4.0: Industrial Internet of Things Platform for Predictive Maintenance.TIP4.0：预测性维护的工业物联网平台。
Sensors (Basel). 2021 Jul 8;21(14):4676. doi: 10.3390/s21144676.

本文引用的文献

1
RUL forecasting for wind turbine predictive maintenance based on deep learning.基于深度学习的风力涡轮机预测性维护的右肺上叶预测
Heliyon. 2024 Oct 15;10(20):e39268. doi: 10.1016/j.heliyon.2024.e39268. eCollection 2024 Oct 30.
2
Development of a Low-Cost Sensor System for Accurate Soil Assessment and Biological Activity Profiling.用于精确土壤评估和生物活性分析的低成本传感器系统的开发。
Micromachines (Basel). 2024 Oct 24;15(11):1293. doi: 10.3390/mi15111293.
3
Multi-Sensor Soil Probe and Machine Learning Modeling for Predicting Soil Properties.
用于预测土壤性质的多传感器土壤探头与机器学习建模
Sensors (Basel). 2024 Oct 25;24(21):6855. doi: 10.3390/s24216855.
4
Intelligent Sensing of Thermal Error of CNC Machine Tool Spindle Based on Multi-Source Information Fusion.基于多源信息融合的数控机床主轴热误差智能传感
Sensors (Basel). 2024 Jun 3;24(11):3614. doi: 10.3390/s24113614.
5
A Vibration Analysis for the Evaluation of Fuel Rail Pressure and Mass Air Flow Sensors on a Diesel Engine: Strategies for Predictive Maintenance.用于评估柴油发动机燃油轨压力和空气质量流量传感器的振动分析：预测性维护策略
Sensors (Basel). 2024 Feb 28;24(5):1551. doi: 10.3390/s24051551.
6
A Review of Finite Element Analysis and Artificial Neural Networks as Failure Pressure Prediction Tools for Corroded Pipelines.有限元分析与人工神经网络作为腐蚀管道失效压力预测工具的综述
Materials (Basel). 2021 Oct 15;14(20):6135. doi: 10.3390/ma14206135.