Description
MC2-442-57CQB-1-1D Панель сенсорного управления – 7 дюймов
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.
GCD207B101 3BHE024642R0101 ABB Processor module
GDD360C 3BHE047217R0101 ABB Processor module
F7123 HIMA 4 Channel Power Distribution Module
DO880-1 3BSE028588R1 ABB 16 channel 24 V digital output module
DMV2400A-CPCI Data acquisition card module
ECU01 ECU01.5 EMG control panel
PMC422FP Ramix Eight Port Serial Controller
CPCI-680 FORCE PowerCore CompactPCI CPU Module
0504994880 ABB SIB V Option Board
2880065-01 PRO-FACE MST Touch screen panel
05701-A-0511 HONEYWELL Frame module
05701-A-0361 HONEYWELL Engineering Card
5X00063G01 Westinghouse COMPANION TO HART ANALOG OUTPUT IO MODULES
05701-A-0351 HONEYWELL Control Card, Single Channel
05701-A-0325 HONEYWELL DC Input Card
05701-A-0326 HONEYWELL FIELD INTERFACE CARD
5SHX1960L0004 ABB IGCT Module
SST-PFB3-VME-2-E SST Network Interface Card
TSXSCP114 Schneider Electric PCMCIA Card for Type III
MS-NAE5510-1 Johnson Network Engine
FBM211 P0914TN FOXBORO Input Interface Module
05701-A-0301 HONEYWELL Single Channel Control Card 4 – 20mA
ETT-VGA-0045 UNIOP HMI Touch Screen Front Overla
CP461-50 Yokogawa Processor Module
12149 ASSY display panel
11994R13 ASSY Communition Module
11993R2 ASSY Analog control card
136188-02 Bently Nevada ETHERNET/RS232 MODBUS I/O MODULE
140XCP51000 Schneider DUMMY MODULE WITH COVER
140XBP00400 Schneider 4-Slot Backplane
140CPU11302 Schneider PROCESSOR 256K RAM 8K USER LOGIC 1XMB
MPC4 200-510-076-114 Vibro Meter Machinery Protection Card meggit
IOCN 200-566-000-112 Meggitt Vibro Meter
7264 AMCI SSI Interface Module
AIP830-111 YOKOGAWA Operating keyboard
REF601 CE446BB1NH ABB Feeder protection
3500/60 163179-01 Bently Nevada Temperature Monitors
IC660BBA104 6231BP10910 GE Analog I/O Block
135473-01 Bently Nevada Proximitor/Seismic Monitor Module
136711-01 Bently Nevada I/O Module With Internal Barriers And Internal Terminations
FEM100 P0973CA FOXBORO Fieldbus Expansion Module
3500-25 149369-01 Bently Nevada Enhanced Keyphasor Module
3500-05-01-02-00-00-01 Bently Nevada 3500/05 System Rack
PDP403 METSO DISTRIBUTED PROCESSING UNIT
PDP401 METSO Distributed Processing Unit Module Card
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