Description
XN-ANBZ-GN Интерфейс человека с сенсорным экраном
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.
30V4060 RELIANCE 3HP 460V AC Drive Version 6 Regulator
1C31116G04 Westinghouse controller
LC100SSP7 LEM 1382 Circuit Board
TU810V1 3BSE013230R1 ABB Compact Module Termination
PPC907BE 3BHE024855R0101 ABB Controller main board
UTLH21 TOSHIBA Controller Module
9907-149 Woodward ProTech 203 Electronic Overspeed Trip Device
HFAS11S TOSHIBA System module
5136-RE2-PCI RELIANCE ELECTRIC INTERFACE MODULE
1756-EN2T Allen-Bradley communication module
MVI69-MNET PROSOFT Modbus TCP/IP Communication Module for CompactLogix
1747-L541 Allen-Bradley SLC 5/04 processor
5X00119G01 19-01-21 Westinghouse Digital quantity input module
5X00121G01 19-01-21 Westinghouse Digital quantity input module
1C31124G01 19-01-21 Westinghouse Digital input module
5X00497G01 19-01-21 Westinghouse The base
1756-RM2 19-01-18 Allen-Bradley ControlLogix Redundancy Module
1746-OW16 19-01-18 Allen-Bradley discrete output module
1X00416H01 WH5-2FF 19-01-18 Westinghouse Process control power module
PSFLT-B2S0151 IDP10-AF1C01F Foxboro I/A Series Pressure Transmitters
MVI56-MCM Allen-Bradley Modbus Communication Module
1756-L73/B Allen-Bradley ControlLogix Controller
TC-PCIC02 HONEYWELL CONTROL INTERFACE MODULE
IK340 HEIDENHAIN Operation station
FBMSVH FOXBORO Ethernet communication module
DSQC658 3HAC025779-001 ABB DeviceNet M/S single
6DD1640-0AH0 Siemens TDC signal assembly
KW3400F Cutler-Hammer TYPE KW FRAME ONLY 3P 400A 660VAC MAX
330104-00-05-10-02-CN Bently Nevada 3300 XL 8 mm Proximity Probes
1794-TB3 Allen-Bradley terminal base unit
1756-TBCH Allen-Bradley ControlLogix Removable Terminal Block (RTB) component
1756-PA75/B Allen-Bradley ControlLogix Power Supply
1756-L61/A Allen-Bradley standard ControlLogix series controller
1756-L61/B Allen-Bradley standard ControlLogix series controller
1756-L72S Allen-Bradley Programmable Automation Controller
3VL9440-7DC30 Siemens release
1756-IF8 Allen-Bradley analog input module
PP845 3BSE042235R1 ABB Operator Panel
3BDH000364R0002 PM783FB0 ABB CPU Module
1C31234G01 Westinghouse Compact Contact Input Module
PW301 Yokogawa Power Module
DR-100-24 MEAN WELL Single Output Industrial DIN Rail Power Supply
1756-PA72C Allen-Bradley ControlLogix Standard Power Supply
1C31179G02 Westinghouse I/O modules
5X00070G04 Westinghouse INPUT MODULE
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