Description
XV-440-12TSB-1-50 Многоточечный сенсорный дисплей EATON
современными требованиями дизайна. Как и XV303, конденсаторный многоточечный сенсорный дисплей поддерживает реализацию
современного пользовательского интерфейса (управление жестами)
и предлагает 7 – и 10 – дюймовые дисплеи, в том числе версии с высоким соотношением сторон 16: 9.
просто и требует меньше компонентов и инженерных работ, чем традиционная проводка. SmartWire – DT интегрирует связь и ввод / вывода
непосредственно в устройства управления, отображения и переключения, открывая новые возможности для инновационных и экономичных решений.
ABB: Запасные части для промышленных роботов серии DSQC, Bailey INFI 90, IGCT, например: 5SHY6545L0001 AC1027001R0101 5SXE10 – 0181, 5SHY3545 L0009, 5SHI3545L0010 3BHB013088 R0001 3BHE009681R0101 GVC750BE101, PM866, PM861K01, PM864, PM510V16, PPD512, PPPD113, PP836A, P865A, 877, PPP881, PPPP885, PPSL500000 4 3BHL00390P0104 5SGY35L4510 и т.д.
General Electric: запасные части, такие как модули, карты и приводы. Например: VMVME – 7807, VMVME – 7750, WES532 – 111, UR6UH, SR469 – P5 – HI – A20, IS230SRTDH2A, IS220PPDAH1B, IS215UCVEH2A, IC698CPE010, IS200SRTDH2ACB и т.д.
Система Bently Nevada: 350 / 3300 / 1900, предохранительные зонды и т.д., например: 3500 / 22M, 3500 / 32, 3500 / 15, 3500 / 23500 / 42M, 1900 / 27 и т.д.
Системы Invis Foxboro: Серия I / A, управление последовательностью FBM, трапециевидное логическое управление, обработка отзыва событий, DAC,
обработка входных / выходных сигналов, передача и обработка данных, такие как FCP270 и FCP280, P0904HA, E69F – TI2 – S, FBM230 / P0926GU, FEM100 / P0973CA и т.д.
Invis Triconex: Модуль питания, модуль CPU, модуль связи, модуль ввода – вывода, например 300830937214351B, 3805E, 831235114355X и т.д.
Вудворд: контроллер местоположения SPC, цифровой контроллер PEAK150, например 8521 – 0312 UG – 10D, 9907 – 149, 9907 – 162, 9907 – 164, 9907 – 167, TG – 13 (8516 – 038), 8440 – 1713 / D, 9907 – 018 2301A, 5466 – 258, 8200 – 226 и т.д.
Hima: модули безопасности, такие как F8650E, F8652X, F8627X, F8678X, F3236, F6217, F6214, Z7138, F8651X, F8650X и т.д.
Honeywell: Все платы DCS, модули, процессоры, такие как: CC – MCAR01, CC – PAIH01, CC – PAIH02, CC – PAIH51, CC – PAIX02, CC – PAON01, CC – PCF901, TC – CR014, TC – PD011, CC – PCNT02 и т.д.
Motorola: серии MVME162, MVME167, MVME172, MVME177, такие как MVME5100, MVME5500 – 0163, VME172PA – 652SE, VME162PA – 344SE – 2G и другие.
Xycom: I / O, платы VME и процессоры, такие как XVME – 530, XVME – 674, XVME – 957, XVME – 976 и т.д.
Коул Морган: Сервоприводы и двигатели, такие как S72402 – NANA, S6201 – 550, S20330 – SRS, CB06551 / PRD – B040SSIB – 63 и т. Д.
Bosch / Luxer / Indramat: модуль ввода / вывода, контроллер PLC, приводной модуль, MSK060C – 0600 – NN – S1 – UP1 – NNN, VT2000 – 52 / R900033828, MHD041B – 144 – PG1 – UN и т.д.
(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.
BDD110-HNLP205879R1 Analog input channel ABB
BCU-02Package INU, XT ABB
BC810K01 ABB Bus interconnect unit suite
ABB BC810 NMD90 Zinc connector
B161S-E176 driver SECO
ABB B5EEd-HENF105082R4 Control card module
ABB B5EC-HENF105077R1 Electric power automation
B3EA-HENF315147R1 Terminating unit ABB
AZ05-0-0-1 AMK driver
ASSY-11994R13 Circuit board module
AS-P892-000 Schneider Single cable RIO interface
AS-B875-002 800 series I/O module Schneider
APBU-44C ABB Branching unit kit APBU
AO2040 Central unit ABB
AO845A Analog output S/R HART 8 channel ABB
AO820 Separate electrical isolation channel ABB
AO810V2-3BSE038415R1 analog output 8 channels ABB
AO810V2 Analog output 8 channels ABB Terminal unit
AO810-3BSE008522R1 Analog output 1×8 channel ABB
AO801 ABB Analog output 8 channels
AO610 ABB Analog output 16Ch
AO202SI Analog output module
ANR5131 Micro laser sensor
AMM42 Control unit YOKOGAWA
AL8XGTE-3 PCI bracket on the circuit board
AIP830-101 Operating keyboard
AIP571-BUS1 YOKOGAWA Automation controller
AIP171 Optical fiber communication system
AIP121-S00 YOKOGAWA output modules
AI830A The input module has 8 channels
AI820 ABB Analog input module
AI810 Analog input 8 channels ABB
AI625 ABB Analog input 16 channels
AGPS-21C ABB POWER SUPPLY
AEM402 Incremental encoder
ACN-CS High performance and multi-function
ACC-5595-208-350-805595-208N Reflective memory hub
5SHY35L45039 Asymmetric Thyristor IGCT ABB
ABB 5SHY35L4503 Asymmetric Thyristor IGCT
AAP3798102-00037 Operation panel
A413654 METSO Control valve
A413240 Controller Quartz Series METSO
A77146-220-51 Control Board
A6824R-9199-00098-13 Vibration sensor EPRO
A6824 Vibration velocity sensor EPRO
A4722-9215KM Sensors and actuators
A5E36717788 ABB Controls and drives IGBT modules
A4H254-8F8T Network module Enterasys
мы организуем фото на складе, чтобы подтвердить
чтобы вернуть их вам. Конечно, мы ответим на ваши озабоченности как можно скорее.
Специально рекомендуемые продукты:
http://www.dcsmodule.ru/product/ic695ecm850-ge-fanuc-controller-carrie/
Reviews
There are no reviews yet.