P-HC-BRC-10000000 Interface Controller

Product parameters

Model: P-HC-BRC-10000000

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

P-HC-BRC-10000000 is mainly used in the field of industrial automation, such as DCS PLCs, industrial controllers, robots, etc.

P-HC-BRC-10000000 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: P-HC-BRC-10000000 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 P-HC-BRC-10000000 correctly on the device and connect the communication interface cable properly.

Please refer to the user manual for operating P-HC-BRC-10000000 to ensure proper use.

Product Introduction

P-HC-BRC-10000000 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 DSRF182AK02 3BSE014078R1
Excitation system ABB module DSRF182
Excitation system ABB module DSRF180M
Excitation system ABB module DSRF180A 57310255-AV
Excitation system ABB module DSRF180A
Excitation system ABB module DSRF160M
Excitation system ABB module DSRF160
Excitation system ABB module DSRF154M
Excitation system ABB module DSRF150
Excitation system ABB module DSRC113
Excitation system ABB module DSRB310
Excitation system ABB module DSRB110
Excitation system ABB module DSQT239
Excitation system ABB module DSQS118
Excitation system ABB module DSQC697
Excitation system ABB module DSQC697
Excitation system ABB module DSQC679 3HAC028357-001
Excitation system ABB module DSQC679 3HAC028357-001
Excitation system ABB module DSQC679
Excitation system ABB module DSQC679
Excitation system ABB module DSQC668 3HAC029157-001
Excitation system ABB module DSQC668
Excitation system ABB module DSQC664
Excitation system ABB module DSQC663 3HAC0298180881
Excitation system ABB module DSQC663 3HAC029818-001/14
Excitation system ABB module DSQC663 3HAC029818-001
Excitation system ABB module DSQC663
Excitation system ABB module DSQC661 3HAC026253-001
Excitation system ABB module DSQC661
Excitation system ABB module DSQC659
Excitation system ABB module DSQC658
Excitation system ABB module DSQC658
Excitation system ABB module DSQC653
Excitation system ABB module DSQC652 3HNA025917-001
Excitation system ABB module DSQC652
Excitation system ABB module DSQC651
Excitation system ABB module DSQC643 3HAC024488-001
Excitation system ABB module DSQC639 3HAC025097-001
Excitation system ABB module DSQC639
Excitation system ABB module DSQC639
Excitation system ABB module DSQC633
Excitation system ABB module DSQC627 3HAC020466-001
Excitation system ABB module DSQC627 3HAC020466-001
Excitation system ABB module DSQC627
Excitation system ABB module DSQC626A
Excitation system ABB module DSQC626
Excitation system ABB module DSQC626
Excitation system ABB module DSQC611
Excitation system ABB module DSQC611
Excitation system ABB module DSQC609
Excitation system ABB module DSQC609
Excitation system ABB module DSQC608
Excitation system ABB module DSQC608
Excitation system ABB module DSQC604
Excitation system ABB module DSQC604
Excitation system ABB module DSQC603
Excitation system ABB module DSQC602
Excitation system ABB module DSQC602
Excitation system ABB module DSQC601 3HAC12815-1
Excitation system ABB module DSQC601
Excitation system ABB module DSQC572
Excitation system ABB module DSQC562 3HAC16014-1/05
Excitation system ABB module DSQC546A
Excitation system ABB module DSQC545A 3HAB8101-19/07A