DI581-S Programmable Secure Numbers

易卖工控网（www.ymgk.com）提供”DI581-S Programmable Secure Numbers”，产品详情：品牌/厂家：ABB +8615359293870、型号：DI581-S+8615359293870、成色：全新、货期：现货 1天内发货、保修：180天，更多产品详情就上易卖工控网。

technical parameter
● Input signal:
Thermocouples: K, S, E, J, T, B, N, etc.
Thermal resistance: Pt100, Cu50, etc.
Resistance: 0-80 Ω, 0-400 Ω, etc.
Voltage: 0-20V... 0-1V → Input impedance ≥ 5M Ω, 0-5V → Input impedance ≥ 100K Ω.
Current: 4-20mA, 0-20mA, etc. → Input resistance ≤ 250 Ω 0-10mA → Input resistance ≤ 500 Ω.
PID control output:
Voltage: 0-5V, 1-5V (load resistance ≥ 200K Ω).
Current: 0-10mA (load resistance ≤ 1000 Ω), 4-20mA, 0-20mA (load resistance ≤ 500 Ω), etc.
Relay: Contact switch output, contact capacity 220VAC/2A or 24VDC/2A.
S SR drive: The drive voltage is 12VDC/30mA (used to drive SSR solid-state relays).
Communication transmission output: 4-20mA, 1-5V, Rs485 communication
Accuracy: ± 0.2% F.S ± 1 word, automatically compensating for temperature drift and time drift
Current output load capacity: 0-600 Ω or 0-250 Ω
Measurement control cycle: 0.5s;
Distribution output: DC24 ± 1V (25mA)
Isolator isolation voltage: Input output: 1500V, input power, output power: 1000V
Working environment: temperature, 0-50 ℃; Relative humidity ≤ 80% RH
Power supply: Switching power supply 100-240VAC (50Hz/60Hz), 24VDC/AC ± 2V Power consumption: ≤ 2W
Appearance color: green
Installation size: 100 × one hundred and twelve × 45 (22.5) mm installation method: DIN35 guide rail



Contact information: 15359213550
Contact person: He Gong
Email: geabbdcs@gmail.com 386353502@qq.com
Official website: https://www.gyamazon.com , http://www.geabb.com



To address the aforementioned challenges, Emerson has provided customers with an intelligent warning system. Due to the fact that the various systems, equipment, process parameters, and equipment data of thermal power plants change with multidimensional boundary conditions, intelligent early warning systems combine mechanism models and big data models to timely identify potential hazards in the production process of power plants.
The big data model deeply mines the relationships between data, constructs an intelligent recognition model, and the intelligent prediction model tracks parameter changes in real time. By comparing and analyzing predicted values with actual values, boundary conditions are identified, and based on equipment operating standards, the health status of parameters is diagnosed and quantitatively evaluated. On the other hand, the mechanism model is based on fixed formulas such as thermal characteristics, performance test data, and equipment input conditions, and comprehensively conducts rating analysis in five aspects: safety, economy, data deviation, system failure, and equipment automatic interlocking input rate.