Description
XV-442-57CQB-1-2AI 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.
IC200MDD844 GE Hybrid input and output modules
Vibro-meter VM600 200-582-915-032 PLC function module
IC660ELB921H GE Single slot PCIM card
FBM208 FOXBORO Digital input module
IC670GBI102D GE Genius interface bus unit module
IC693CHS397 GE 5-slot expansion board
IC693CHS398 GE 5-slot expansion board
IC693CPU350 GE 90-30 Series Processor Module
FBM240 FOXBORO Driver motherboard
FBM212 FOXBORO CPU processor
IC693CPU363-CH GE Single slot CPU module with embedded Ethernet interface
P0973LN FOXBORO Frequency converter motherboard
IC693MDR390 GE 4 Amp Isolated Relay Output Module
IC693MDR390 GE Combined Discrete I/O Modules
FBM219 FOXBORO input module
P0904AK FOXBORO Counting template
IC695CHS012 GE RX3i series 12 slot universal backplane
IC695CPE330 GE RX3i CPE330 controller
FBM215 FOXBORO Signal processing board
P0924JH FOXBORO Power supply panel
IS200TSVOH1B GE Terminal board
IC697PWR724 GE Power module
IC697PCM711P GE Single slot programmable coprocessor (PCU)
IC697MDL750H GE Discrete output module
PCI-8517 NI FlexRay interface device
RER133 ABB Bus connection module
P0916DC FOXBORO analog input module
SR469-P5-HI-A20-T GE SR469 multi line relay
FBM223 FOXBORO PLC module
THED136100WL GE Thermal magnetic circuit breaker
FBM233 FOXBORO DCS card piece
UR1HH GE Power module GE multi wire UR series universal relay
FBM223 Foxboro Communication board
FBM222 FOXBORO Input output module
FBM216B P0927AJ Foxboro driver module
FPS400-24 FOXBORO output module
VMIVME-7698-345-350-017698-345-B GE Single board computer
FBM216B FOXBORO Control system module
8602-FT-ST GE I/O module
P0916AA FOXBORO Commissioning cable
P0926KP FOXBORO cable
DKS11.1-040-7-FW Rexroth SERVO DRIVE
TVB-1202-1/ANET 1381-647980-12 Circuit board module
870ITEC-AYFNZ-7 FOXBORO CPU module
TVB6002-1IMC-1308-644857-12-1381-644857-16 Circuit board module
TVB6002-1/IMC 1308-644857-12 Circuit board module
FBM224 P0926GG FOXBORO Control panel
VME-U10/B 381-641697-5 Circuit board module
P0916PH P0916JS FOXBORO Inverter circuit board
873EC-JIPFGZ FOXBORO Controller master unit
FEM100 P0973CA FOXBORO Controller module
FBM227 P0927AC FOXBORO Simulation module
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