Description
XV-442-57CQB-1-50 Экран 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.
CX11000001 Beckhoff POWER SUPPLY UNIT FOR CX1000
F8627 HIMA Ethernet communication module
DC10-12P0-0000 WATLOW SCR POWER SWITCHING DEVICE
H51q-HRS B5233-2 997205233 HIMA Safety System Module
IS200JPDFG1A GE Mark VI printed circuit board
MSPC-68866800 ABB Circuit board module
PPU-3 DEIF Paralleling & protection unit
pw502 YOKOGAWA power-supply module
MT8803G Anritsu Testset
REX521GHHGSH51G ABB PROTECTION UNIT
SPAJ140C-AA ABB COMBINED OVERCURRENT AND EARTH-FAULT RELAY
TB810 ABB Modulebus Optical Port
SCC-C 23070-0-10310110 ABB Electronic gas condenser
TB820V2 3BSE013208R1 ABB process I/O system
V18345-1010121001 ABB TZIDC Electro-Pneumatic Positioner
VE6041F01C1 EMERSON Intelligent switch
Z7128 HIMA CABLE PLUG
2N3A8204-B TOSHIBA PC BOARD ASSEMBLY
4PP220.0571-45 B&R Power Panel PP220 5.7″ QVGA color LC-display with touch screen
YNT511D YOKOGAWA Repeater module
4PP220.0571-65 B&R Power Panel PP220 5.7″ QVGA color LC-display with touch screen
3500/33-01-01 Bently Nevada 16-Channel Relay Module
0090-76110 AMAT PCB board
CI871 3BSE092693R1 ABB AC 800M communication interface
FCP280 RH924YA FOXBORO Field Control Processor
IC670ALG310 GE isolated analog output module
IS220PDIAH1A 336A4940CSP1 GE I/O Pack
IW93-2 HESG440356R1 HESG216678B ABB Circuit Board
MVME162-213 MOTOROLA Embedded Controller
M128-010 M128-010-A001B MOOG Controller module
MPS022 13100-203 SCHROFF power-supply module
PFSA140 3BSE006503R1 ABB Roll Supply Unit
PFTL301E 3BSE019050R1000 1.0KN ABB Load cell
PFEA111-20 ABB Tension Electronics
PM851AK01 ABB Controller module
PM856K01 ABB Processor Unit
PM860K01 ABB Processor Unit Kit
PP885 3BSE069276R1 ABB Touch Panel 15,4″
REF610C11LCLR ABB feeder IED
PPD113 PPD103B101 ABB Excitation control system
RMP201-8 KONGSBERG Remote MultiPurpose Input / Output
SBRIO-9607 NI embedded controller
RMP200-8 KONGSBERG DIGITAL OUTPUT MODULE
SPBRC410 ABB Controller with Modbus TCP Interface
ZMI-2002 ZYGO VME Measurement Board
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