Description
UFC760BE42 3BHE004573R1042 Контроллер ABB
Швейцария, и входит в десятку крупнейших швейцарских транснациональных корпораций.UFC760BE42 3BHE004573R1042
химическая, нефтехимическая, фармацевтическая, целлюлозно – бумажная, нефтепереработка; Оборудование приборов: электронные приборы, телевизоры и оборудование для передачи данных,
генераторы, гидротехнические сооружения; Каналы связи: интегрированные системы, системы сбора и распространения;UFC760BE42 3BHE004573R1042Строительная промышленность: коммерческое и промышленное строительство.
0 Preface
Germany”s “Industry 4.0″ and the United States” “Industrial Internet” will
restructure the world”s industrial layout and economic structure, bringing different challenges and
opportunities to countries around the world. The State Council of China issued “Made in China 2025” as an action plan
for the first ten years of implementing the strategy of manufacturing a strong country, which will accelerate the integrated
development of IoT technology and manufacturing technology [1]. IoT collects data on machine operations, material usage
, facility logistics, etc., bringing transparency to operators. This transparency is brought about by the application of data analytics,
which refers to the use of statistical and machine learning methods to discover different data characteristics and patterns. Machine
learning technology is increasingly used in various manufacturing applications, such as predictive maintenance, test time reduction,
supply chain optimization, and process optimization, etc. [2-4]. The manufacturing process of enterprises has gradually developed from
the traditional “black box” model to the “multi-dimensional, transparent and ubiquitous perception” model [5].
1 Challenges facing manufacturing analysis
The goal of manufacturing analytics is to increase productivity by reducing costs without compromising quality:
(1) Reduce test time and calibration, including predicting test results and calibration parameters;
(2) Improve quality and reduce the cost of producing scrap (bad parts) by identifying the root causes of scrap and optimizing
the production line on its own;
(3) Reduce warranty costs, use quality testing and process data to predict field failures, and cross-value stream analysis;
(4) Increase throughput, benchmark across production lines and plants, improve first-pass rates, improve first-pass throughput,
and identify the cause of performance bottlenecks such as overall equipment effectiveness (OEE) or cycle time;
Vibro-meter VM600 204-040-100-013
Vibro-meter VM600 200-566-000-013
Vibro-meter VM600 200-530-025-014
Vibro-meter VM600 200-530-022-014
Vibro-meter VM600 200-595-045-114
Vibro-meter VM600 200-530-026-014
Vibro-meter VM600 200-595-072-322
Vibro-meter VM600 200-565-000-013
IS200VPROH2B GE
GE 369-HI-0-0-0-0-H-E
Vibro-meter VM600 MPC4
Vibro-meter 200-510-111-013
Vibro-meter 200-510-017-019
200-510-017-019 VM600 MPC4
200-510-017-019 200-510-111-013
200-510-017-019 200-510-111-013 VM600 MPC4
Vibro-meter 200-560-000-016
Vibro-meter VM600 IOC4T
Vibro-meter 200-560-101-015
Vibro-meter 200-560-000-018
200-560-000-018 VM600 IOC4T
200-560-000-018 200-560-101-015
200-560-000-018 200-560-101-015 VM600 IOC4T
Vibro-meter 200-560-000-113 VM600
Vibro-meter VM600 IOCN
Vibro-meter 200-566-101-012
Vibro-meter 200-566-000-012
Vibro-meter VM600 IOCN
200-566-000-012 VM600 IOCN
200-566-000-012 200-566-101-012
200-566-000-012 200-566-101-012 VM600 IOCN
Vibro-meter 200-566-000-012
Vibro-meter 200-570-101-013 VM600
Vibro-meter 200-570-000-014
200-570-000-014 200-570-101-013 VM600
Vibro-meter 200-582-915-032
Vibro-meter 200-582-915-032 VM600
Vibro-meter 200-595-002-011
Vibro-meter 200-595-031-111
Vibro-meter 200-595-031-111 VM600 CPUM
Vibro-meter VK5488E-107S
Vibro-meter VM600 RLC16
Vibro-meter 200-582-200-011
VM600 RPS6U 200-582-200-011
VIBRO-METER 200-582-500-013
VM600 RPS6U 200-582-500-013
Vibro-meter VM600 RPS6U
Vibro-meter VM600 RPS6U 113-40060
VM600-ABE040 Vibro-meter
Reviews
There are no reviews yet.