• 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

智能异常检测和预测，符合工业 4.0 标准的装配过程维护。

Smart Anomaly Detection and Prediction for Assembly Process Maintenance in Compliance with Industry 4.0.

机构信息

Faculty of Materials Science and Technology in Trnava, Slovak University of Technology in Bratislava, 917 24 Trnava, Slovakia.

出版信息

Sensors (Basel). 2021 Mar 29;21(7):2376. doi: 10.3390/s21072376.

DOI:10.3390/s21072376
PMID:33805557
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC8037397/
Abstract

One of the big problems of today's manufacturing companies is the risks of the assembly line unexpected cessation. Although planned and well-performed maintenance will significantly reduce many of these risks, there are still anomalies that cannot be resolved within standard maintenance approaches. In our paper, we aim to solve the problem of accidental carrier bearings damage on an assembly conveyor. Sometimes the bearing of one of the carrier wheels is seized, causing the conveyor, and of course the whole assembly process, to halt. Applying standard approaches in this case does not bring any visible improvement. Therefore, it is necessary to propose and implement a unique approach that incorporates Industrial Internet of Things (IIoT) devices, neural networks, and sound analysis, for the purpose of predicting anomalies. This proposal uses the mentioned approaches in such a way that the gradual integration eliminates the disadvantages of individual approaches while highlighting and preserving the benefits of our solution. As a result, we have created and deployed a smart system that is able to detect and predict arising anomalies and achieve significant reduction in unexpected production cessation.

摘要

当今制造企业面临的一个大问题是装配线意外停止的风险。尽管计划周密、执行良好的维护将显著降低许多此类风险，但仍有一些异常情况无法通过标准维护方法解决。在本文中，我们旨在解决装配传送带上意外的承载轴承损坏问题。有时，其中一个承载轮的轴承被卡住，导致传送带，当然还有整个装配过程停止。在这种情况下，应用标准方法并没有带来任何明显的改进。因此，有必要提出并实施一种独特的方法，该方法结合了工业物联网 (IIoT) 设备、神经网络和声音分析，以预测异常。该提案以这样一种方式使用所述方法，即逐渐集成消除了个别方法的缺点，同时突出并保留了我们解决方案的优势。因此，我们创建并部署了一个智能系统，能够检测和预测出现的异常，并显著减少意外停产。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/517e/8037397/c5851d3b32ae/sensors-21-02376-g015.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/517e/8037397/396ac9293bb5/sensors-21-02376-g0A1.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/517e/8037397/20f21ceafa06/sensors-21-02376-g0A2.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/517e/8037397/a82789bbd286/sensors-21-02376-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/517e/8037397/807ba4a8214b/sensors-21-02376-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/517e/8037397/a3316e37bbda/sensors-21-02376-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/517e/8037397/5efa311ad1c8/sensors-21-02376-g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/517e/8037397/113f54ef34d4/sensors-21-02376-g005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/517e/8037397/fe306ac5e152/sensors-21-02376-g006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/517e/8037397/2809888f38c1/sensors-21-02376-g007.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/517e/8037397/894a3cf48992/sensors-21-02376-g008.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/517e/8037397/58d629f82167/sensors-21-02376-g009.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/517e/8037397/18ad5a143598/sensors-21-02376-g010.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/517e/8037397/723b204770fa/sensors-21-02376-g011.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/517e/8037397/8d5c7de72079/sensors-21-02376-g012.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/517e/8037397/2c26615d3006/sensors-21-02376-g013.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/517e/8037397/3cee039d6bdb/sensors-21-02376-g014.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/517e/8037397/c5851d3b32ae/sensors-21-02376-g015.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/517e/8037397/396ac9293bb5/sensors-21-02376-g0A1.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/517e/8037397/20f21ceafa06/sensors-21-02376-g0A2.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/517e/8037397/a82789bbd286/sensors-21-02376-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/517e/8037397/807ba4a8214b/sensors-21-02376-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/517e/8037397/a3316e37bbda/sensors-21-02376-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/517e/8037397/5efa311ad1c8/sensors-21-02376-g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/517e/8037397/113f54ef34d4/sensors-21-02376-g005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/517e/8037397/fe306ac5e152/sensors-21-02376-g006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/517e/8037397/2809888f38c1/sensors-21-02376-g007.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/517e/8037397/894a3cf48992/sensors-21-02376-g008.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/517e/8037397/58d629f82167/sensors-21-02376-g009.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/517e/8037397/18ad5a143598/sensors-21-02376-g010.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/517e/8037397/723b204770fa/sensors-21-02376-g011.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/517e/8037397/8d5c7de72079/sensors-21-02376-g012.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/517e/8037397/2c26615d3006/sensors-21-02376-g013.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/517e/8037397/3cee039d6bdb/sensors-21-02376-g014.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/517e/8037397/c5851d3b32ae/sensors-21-02376-g015.jpg

相似文献

1
Smart Anomaly Detection and Prediction for Assembly Process Maintenance in Compliance with Industry 4.0.智能异常检测和预测，符合工业 4.0 标准的装配过程维护。
Sensors (Basel). 2021 Mar 29;21(7):2376. doi: 10.3390/s21072376.
2
Edge-to-Cloud IIoT for Condition Monitoring in Manufacturing Systems with Ubiquitous Smart Sensors.边缘到云的 IIoT 用于制造系统中的状态监测，采用无处不在的智能传感器。
Sensors (Basel). 2022 Aug 7;22(15):5901. doi: 10.3390/s22155901.
3
Standard Connections for IIoT Empowered Smart Manufacturing.工业物联网赋能智能制造的标准连接
Manuf Lett. 2020 Oct;26. doi: 10.1016/j.mfglet.2020.08.006.
4
Scalable Fleet Monitoring and Visualization for Smart Machine Maintenance and Industrial IoT Applications.用于智能机器维护和工业物联网应用的可扩展机队监控与可视化
Sensors (Basel). 2020 Aug 2;20(15):4308. doi: 10.3390/s20154308.
5
..
Sensors (Basel). 2018 Sep 23;18(10):3215. doi: 10.3390/s18103215.
6
Proposal for an IIoT Device Solution According to Industry 4.0 Concept.根据工业 4.0 概念的物联网设备解决方案提案。
Sensors (Basel). 2022 Jan 2;22(1):325. doi: 10.3390/s22010325.
7
An Analytics Environment Architecture for Industrial Cyber-Physical Systems Big Data Solutions.工业网络物理系统大数据解决方案的分析环境架构。
Sensors (Basel). 2021 Jun 23;21(13):4282. doi: 10.3390/s21134282.
8
Improved Mitigation of Cyber Threats in IIoT for Smart Cities: A New-Era Approach and Scheme.工业物联网中智慧城市的网络威胁缓解技术改进：一种新的时代方法与方案。
Sensors (Basel). 2021 Mar 11;21(6):1976. doi: 10.3390/s21061976.
9
Open Source IIoT Solution for Gas Waste Monitoring in Smart Factory.开源工业物联网解决方案，用于智能工厂中的废气监测。
Sensors (Basel). 2022 Apr 13;22(8):2972. doi: 10.3390/s22082972.
10
RI2AP: Robust and Interpretable 2D Anomaly Prediction in Assembly Pipelines.RI2AP：装配流水线中稳健且可解释的二维异常预测
Sensors (Basel). 2024 May 20;24(10):3244. doi: 10.3390/s24103244.

引用本文的文献

1
Predictive maintenance in Industry 4.0: a survey of planning models and machine learning techniques.工业4.0中的预测性维护：规划模型与机器学习技术综述
PeerJ Comput Sci. 2024 May 14;10:e2016. doi: 10.7717/peerj-cs.2016. eCollection 2024.
2
RI2AP: Robust and Interpretable 2D Anomaly Prediction in Assembly Pipelines.RI2AP：装配流水线中稳健且可解释的二维异常预测
Sensors (Basel). 2024 May 20;24(10):3244. doi: 10.3390/s24103244.
3
Anomaly Detection in a Smart Industrial Machinery Plant Using IoT and Machine Learning.利用物联网和机器学习在智能工业机械工厂中进行异常检测。

本文引用的文献

1
Anomaly Detection Neural Network with Dual Auto-Encoders GAN and Its Industrial Inspection Applications.基于双自动编码器 GAN 的异常检测神经网络及其工业检测应用。
Sensors (Basel). 2020 Jun 12;20(12):3336. doi: 10.3390/s20123336.
2
The Use of Artificial Neural Networks for Forecasting of Air Temperature inside a Heated Foil Tunnel.利用人工神经网络预测加热箔隧道内的空气温度。
Sensors (Basel). 2020 Jan 24;20(3):652. doi: 10.3390/s20030652.
3
A Novel Fault Detection Method for Rolling Bearings Based on Non-Stationary Vibration Signature Analysis.
Sensors (Basel). 2023 Oct 7;23(19):8286. doi: 10.3390/s23198286.
4
Systematic Literature Review on Visual Analytics of Predictive Maintenance in the Manufacturing Industry.系统文献综述：制造业预测性维护的可视化分析。
Sensors (Basel). 2022 Aug 23;22(17):6321. doi: 10.3390/s22176321.
5
Edge-to-Cloud IIoT for Condition Monitoring in Manufacturing Systems with Ubiquitous Smart Sensors.边缘到云的 IIoT 用于制造系统中的状态监测，采用无处不在的智能传感器。
Sensors (Basel). 2022 Aug 7;22(15):5901. doi: 10.3390/s22155901.
6
Robust Assembly Assistance Using Informed Tree Search with Markov Chains.基于马尔可夫链的信息树搜索的鲁棒装配辅助。
Sensors (Basel). 2022 Jan 10;22(2):495. doi: 10.3390/s22020495.
7
Identifying and Characterizing Conveyor Belt Longitudinal Rip by 3D Point Cloud Processing.通过三维点云处理识别和表征输送带纵向撕裂
Sensors (Basel). 2021 Oct 7;21(19):6650. doi: 10.3390/s21196650.
8
Analyzing Particularities of Sensor Datasets for Supporting Data Understanding and Preparation.分析传感器数据集的特点，以支持数据理解和准备。
Sensors (Basel). 2021 Sep 10;21(18):6063. doi: 10.3390/s21186063.
9
Sound Detection Monitoring Tool in CNC Milling Sounds by K-Means Clustering Algorithm.基于 K-Means 聚类算法的数控机床铣削声音的声音检测监测工具。
Sensors (Basel). 2021 Jun 23;21(13):4288. doi: 10.3390/s21134288.
基于非平稳振动特征分析的滚动轴承新型故障检测方法。
Sensors (Basel). 2019 Sep 16;19(18):3994. doi: 10.3390/s19183994.
4
Artificial Neural Network Application for Current Sensors Fault Detection in the Vector Controlled Induction Motor Drive.人工神经网络在矢量控制感应电动机驱动中电流传感器故障检测的应用。
Sensors (Basel). 2019 Jan 29;19(3):571. doi: 10.3390/s19030571.
5
Data splitting for artificial neural networks using SOM-based stratified sampling.基于 SOM 的分层抽样的人工神经网络数据拆分。
Neural Netw. 2010 Mar;23(2):283-94. doi: 10.1016/j.neunet.2009.11.009. Epub 2009 Nov 26.