Intelligent Embedded Monitoring System of Hydraulic CNC Machine Tool
This paper designs the hydraulic CNC machine tool monitoring system based on the intelligent embedded theory. The mass data generated during the operation of the equipment is collected via the network. The diagnosis expert system is used to interpret these state data to achieve pre-judgment of fault...
Main Authors: | , , , |
---|---|
Format: | Article |
Language: | English |
Published: |
AIDIC Servizi S.r.l.
2017-12-01
|
Series: | Chemical Engineering Transactions |
Online Access: | https://www.cetjournal.it/index.php/cet/article/view/941 |
id |
doaj-cfef31be863d446685cd6139b0678d47 |
---|---|
record_format |
Article |
spelling |
doaj-cfef31be863d446685cd6139b0678d472021-02-17T21:16:02ZengAIDIC Servizi S.r.l.Chemical Engineering Transactions2283-92162017-12-016210.3303/CET1762143Intelligent Embedded Monitoring System of Hydraulic CNC Machine Tool Dongmei GongFeng XuJianshu LiuLiang XuanThis paper designs the hydraulic CNC machine tool monitoring system based on the intelligent embedded theory. The mass data generated during the operation of the equipment is collected via the network. The diagnosis expert system is used to interpret these state data to achieve pre-judgment of fault, improve the equipment reliability and reduce the operating cost. The high-frequency network-based servo data sampling technology is developed using FANUC open Focas dynamic link database. The storage and management methods based on big data are studied. The upper layer data management framework is built. Open-source Historian real-time database is used for data mining. Finally, the diagnosis model is established to interpret the abstract data, and establish a relationship with the machine failure mode. The model of servo lean energy consumption is obtained by studying the energy consumption under different modes of CNC machine tool to optimize the energy consumption. https://www.cetjournal.it/index.php/cet/article/view/941 |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Dongmei Gong Feng Xu Jianshu Liu Liang Xuan |
spellingShingle |
Dongmei Gong Feng Xu Jianshu Liu Liang Xuan Intelligent Embedded Monitoring System of Hydraulic CNC Machine Tool Chemical Engineering Transactions |
author_facet |
Dongmei Gong Feng Xu Jianshu Liu Liang Xuan |
author_sort |
Dongmei Gong |
title |
Intelligent Embedded Monitoring System of Hydraulic CNC Machine Tool
|
title_short |
Intelligent Embedded Monitoring System of Hydraulic CNC Machine Tool
|
title_full |
Intelligent Embedded Monitoring System of Hydraulic CNC Machine Tool
|
title_fullStr |
Intelligent Embedded Monitoring System of Hydraulic CNC Machine Tool
|
title_full_unstemmed |
Intelligent Embedded Monitoring System of Hydraulic CNC Machine Tool
|
title_sort |
intelligent embedded monitoring system of hydraulic cnc machine tool |
publisher |
AIDIC Servizi S.r.l. |
series |
Chemical Engineering Transactions |
issn |
2283-9216 |
publishDate |
2017-12-01 |
description |
This paper designs the hydraulic CNC machine tool monitoring system based on the intelligent embedded theory. The mass data generated during the operation of the equipment is collected via the network. The diagnosis expert system is used to interpret these state data to achieve pre-judgment of fault, improve the equipment reliability and reduce the operating cost. The high-frequency network-based servo data sampling technology is developed using FANUC open Focas dynamic link database. The storage and management methods based on big data are studied. The upper layer data management framework is built. Open-source Historian real-time database is used for data mining. Finally, the diagnosis model is established to interpret the abstract data, and establish a relationship with the machine failure mode. The model of servo lean energy consumption is obtained by studying the energy consumption under different modes of CNC machine tool to optimize the energy consumption.
|
url |
https://www.cetjournal.it/index.php/cet/article/view/941 |
work_keys_str_mv |
AT dongmeigong intelligentembeddedmonitoringsystemofhydrauliccncmachinetool AT fengxu intelligentembeddedmonitoringsystemofhydrauliccncmachinetool AT jianshuliu intelligentembeddedmonitoringsystemofhydrauliccncmachinetool AT liangxuan intelligentembeddedmonitoringsystemofhydrauliccncmachinetool |
_version_ |
1724264326695485440 |