CI855K01 DCS excitation system

Product parameters

Model: CI855K01

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

CI855K01 is mainly used in the field of industrial automation, such as DCS PLCs, industrial controllers, robots, etc.

CI855K01 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: CI855K01 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 CI855K01 correctly on the device and connect the communication interface cable properly.

Please refer to the user manual for operating CI855K01 to ensure proper use.

Product Introduction

CI855K01 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.

149992-01 3500/33 Spare 16-Channel Relay Output Module
9199-00002 A6120 EMERSON Control card piece
135489-01 3500/42M  I/O module
125840-01 3500/15  High Voltage AC Power Input Module
330130-045-00-00  3300 XL Extension Cable
135489-03 3500/42M  I/O Module with Internal Barriers (4 Velomitor channels) and Internal Terminations
9199-00003 A6210 EMERSON I/O/DO module
125840-01 3500/15  High Voltage AC Power Input Module
125768-01 3500/20  RIM I/O Module
149992-01  Spare 16-Channel Relay Output Module
A3120/022-000 CSI3120 EMERSON Bearing-Vibration Monitor
3500/60  Resistance temperature detector
1900/55  General Purpose Equipment Monitor
3500/33 16 Channel Relay Module
330101-00-25-10-02-00  3300 XL 8 mm Proximity Probes, Metric
330130-040-00-00 3300 XL Standard Extension Cable
330130-085-00-05 3300 XL Standard Extension Cable
330180-X1-05 Modified Proximitor Sensor
A6110 9199-00001 EMERSON I/O/DO module
3500/25 Enhanced Keyphasor Module
3500/20 Rack Interface Module
3500/93 series system display
3500/42M Position Monitor
3500/53M  Electronic Overspeed Detection System
A6140 9199-00058 emerson Digital I/O module
3500/32 4-channel relay module
3500/45  4-channel  Position Monitor
3500/05-02-02 Axial displacement probe
A6220 9199-00009 EMERSON Ethernet module
135489-04  I/O Module With Internal Barriers And Internal Terminations
136703-01  3500/50 I/O Module with Internal Barriers and Internal Termination
135473-01 Proximitor/Seismic Monitor Module
125840-01  High Voltage AC Power Input Module
133396-01  Overspeed Detection I/O Module
138708-01 Shaft Absolute I/O Module with Internal Terminations
125760-01 Data Manager I/O Module
A6370D EMERSON Analog quantity input card
125680-01  Proximitor I/O Module
330980-51-05 XL NSv Proximitor Sensor
330850-90-05  3300 XL 25 mm Proximitor Sensor
136180-01 Communication Gateaway Module
149369-01  Enhanced Keyphasor Module
133388-01  3500/53 Overspeed Detection Module
A6410 EMERSON Output module
127610-01  3500/15 AC Power Supply Module
176449-02 Proximitor/Seismic Monitor
125744-02 Rack Interface Module