ARC093AV1 Multifunctional control board

Product parameters

Model: ARC093AV1

Brand: ABB

Size: 10cm x 10cm x 30cm

Weight: 1.2KG

Color: White Black

Working voltage: 5V

Working temperature: -10 ℃ to 50 ℃

Communication interface: RS232, RS485, CAN

Product specifications

Support for MODBUS protocol

Support hardware flow control

Using high-speed CMOS devices

Support for data caching

application area

ARC093AV1 is mainly used in the field of industrial automation, such as DCS PLCs, industrial controllers, robots, etc.

ARC093AV1 is mainly used in steel manufacturing, thermal power generation, hydroelectric power generation, glass manufacturing, paper mills, cement factories, petrochemicals

Chemical fiber, pharmaceutical manufacturing, rubber, plastics, ferrous metal food, machine tools, specialized equipment, transportation vehicles, mechanical equipment

Electronic communication equipment, instruments and beverages, tobacco processing, clothing, textiles, leather, wood processing, furniture, printing, etc

Model: ARC093AV1 Brand: ABB Archive Function: Communication Function Product Certification: Qualified

Working voltage: 24V Internal variable: No pattern Type: Vector diagram Applicable range: Industrial type

Number of screens: 4 Imported or not: Yes Customized for processing: No

Alarm function: Product name: Module Input voltage: 24V Rated current: 5A

Special service: including postage. Remarks: one-year warranty. Operation method: remote control

Applicable motor: servo motor system memory: 8MB Display color: 99.9 color gamut

Power voltage: 220V Input method: Current Input Material code: 65218

Communication interface: HDMI interface Working temperature: 60 Material number 26854 Other functions Analog communication

Output frequency: 50HZ Rated voltage: 24V Alarm type: Light alarm

Disconnect capacity: 0.01 Vector graph support: Minimum number of packages supported: 1

Display type: IPS USB interface quantity: 2 interfaces Memory card slot: 2

Panel protection level: 3 Memory expansion capacity: 128MB User script support: supported

Speed response frequency: 1MS Working environment temperature: -40 to 100 Insulation withstand voltage test: Passed

Third party communication products: ABB controllers available for sale in: nationwide

Product Instructions

Please install ARC093AV1 correctly on the device and connect the communication interface cable properly.

Please refer to the user manual for operating ARC093AV1 to ensure proper use.

Product Introduction

ARC093AV1 is a high-performance industrial automation communication module that uses high-speed CMOS devices, supports MODBUS protocol, and supports
hardware flow control. It is an ideal choice in the field of industrial automation.

(5) Perform predictive maintenance, analyze machine operating conditions, determine the main
causes of failures, and predict component failures to avoid unplanned downtime.

Traditional quality improvement programs include Six Sigma, Deming Cycle, Total Quality Management (TQM), and Dorian Scheinin’s
Statistical Engineering (SE) [6]. Methods developed in the 1980s and 1990s are typically applied to small amounts
of data and find univariate relationships between participating factors. The use of the MapReduce paradigm to simplify data processing in
large data sets and its further development have led to the mainstream proliferation of big data analytics [7]. Along with the development of
machine learning technology, the development of big data analytics has provided a series of new tools that can be applied to manufacturing
analysis. These capabilities include the ability to analyze gigabytes of data in batch and streaming modes, the ability to find complex multivariate
nonlinear relationships among many variables, and machine learning algorithms that separate causation from correlation.

Millions of parts are produced on production lines, and data on thousands of process and quality measurements are collected for them, which is
important for improving quality and reducing costs. Design of experiments (DoE), which repeatedly explores thousands of causes through
controlled experiments, is often too time-consuming and costly. Manufacturing experts rely on their domain knowledge to detect key
factors that may affect quality and then run
DoEs based on these factors. Advances in big data analytics and machine learning enable the detection of critical factors that effectively
impact quality and yield. This, combined with domain knowledge, enables rapid detection of root causes of failures. However,
there are some unique data science challenges in manufacturing.

(1) Unequal costs of false alarms and false negatives. When calculating accuracy, it must be recognized that false alarms
and false negatives may have unequal costs. Suppose a false negative is a bad part/instance that was wrongly predicted to
be good. Additionally, assume that a false alarm is a good part that was incorrectly predicted as bad. Assuming further that
the parts produced are safety critical, incorrectly predicting that bad parts are good (false negatives) can put human lives
at risk. Therefore, false negatives can be much more costly than false alarms. This trade-off needs to be considered when
translating business goals into technical goals and candidate evaluation methods.

Excitation system ABB module 3HAC6428-1/04
Excitation system ABB module 3HAC6428-1/04
Excitation system ABB module 3HAC6314-1
Excitation system ABB module 3HAC6268-1
Excitation system ABB module 3HAC6157-1
Excitation system ABB module 3HAC6157-1
Excitation system ABB module 3HAC5981-1
Excitation system ABB module 3HAC5948-1
Excitation system ABB module 3HAC5947-1
Excitation system ABB module 3HAC5902-1
Excitation system ABB module 3HAC5889-1
Excitation system ABB module 3HAC5885-1
Excitation system ABB module 3HAC5824-10
Excitation system ABB module 3HAC5704-1
Excitation system ABB module 3HAC5700-1
Excitation system ABB module 3HAC5681-1
Excitation system ABB module 3HAC5566-1
Excitation system ABB module 3HAC5566-1
Excitation system ABB module 3HAC5548-2
Excitation system ABB module 3HAC5518-1
Excitation system ABB module 3HAC5518-1
Excitation system ABB module 3HAC5507-1
Excitation system ABB module 3HAC5498-1
Excitation system ABB module 3HAC5498-1
Excitation system ABB module 3HAC5497-1
Excitation system ABB module 3HAC5497-1
Excitation system ABB module 3HAC5463-1
Excitation system ABB module 3HAC5347-1
Excitation system ABB module 3HAC5246-7
Excitation system ABB module 3HAC5246-65
Excitation system ABB module 3HAC5246-2
Excitation system ABB module 3HAC5025-1
Excitation system ABB module 3HAC5021-1
Excitation system ABB module 3HAC4837-2
Excitation system ABB module 3HAC4836-3
Excitation system ABB module 3HAC4803-2
Excitation system ABB module 3HAC4758-1
Excitation system ABB module 3HAC4738-6
Excitation system ABB module 3HAC4738-34
Excitation system ABB module 3HAC4738-26
Excitation system ABB module 3HAC4731-6
Excitation system ABB module 3HAC4627-1
Excitation system ABB module 3HAC4562-1
Excitation system ABB module 3HAC4561-1
Excitation system ABB module 3HAC4339-2
Excitation system ABB module 3HAC4314-7
Excitation system ABB module 3HAC4272-1
Excitation system ABB module 3HAC4247-5
Excitation system ABB module 3HAC4247-2
Excitation system ABB module 3HAC4232-1
Excitation system ABB module 3HAC4127-6
Excitation system ABB module 3HAC4125-1
Excitation system ABB module 3HAC4124-7
Excitation system ABB module 3HAC4057-6
Excitation system ABB module 3HAC4057-2
Excitation system ABB module 3HAC3961-1
Excitation system ABB module 3HAC3865-96
Excitation system ABB module 3HAC3865-45
Excitation system ABB module 3HAC3865-34
Excitation system ABB module 3HAC3865-16
Excitation system ABB module 3HAC3838-4
Excitation system ABB module 3HAC3838-19
Excitation system ABB module 3HAC3838-17
Excitation system ABB module 3HAC3697-1
Excitation system ABB module 3HAC3619-1
Excitation system ABB module 3HAC3616-1/08
Excitation system ABB module 3HAC3517-1
Excitation system ABB module 3HAC3403-1
Excitation system ABB module 3HAC3389-1
Excitation system ABB module 3HAC3333-1