Description
IC694BEM331 Horner Electric
высотой 3U, расположенный в раме управления под DSPX.
волоконно – оптический разъем на передней панели и передаются в модуль обнаружения заземления.
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.
MC-TDOD63 HONEYWELL 51309154-275 MU-TDOD63 Digital Output 31-200 Vdc Solid-State
MC-TDOD54 HONEYWELL MU-TDOD54 Digital Output 3-30 Vdc Solid-State FTA
MC-TDOD53 HONEYWELL 51304650-250 Digital Output FTA SS relay
MC-TDOA53 51304648-275 HONEYWELL MU-TDOA53 Digital Output
MC-TDIY62 HONEYWELL MU-TDIY62 Digital Input 24 Vdc FTA
MC-TDID72 HONEYWELL MU-TDIA62 Digital Input 24 Vdc FTA
MC-TDID52 HONEYWELL MU-TDIA72 Digital Input 24 Vdc FTA
MC-TDIA72 HONEYWELL MU-TDIA72 Digital Input lsolated 120 Vac FTA -Packaged
MC-TDIA52 HONEYWELL Digital Input lsolated 120 Vac FTA MU-TDIA52
MC-TAOY53 HONEYWELL MU-TAOY52 Analog Output 16 FTA
MC-TAOY52 HONEYWELL Analog Output 16 FTA MU-TAOY52
MC-TAOX52 HONEYWELL Analog Output FTA MU-TAOX52
MC-TPIX52 HONEYWELL Pulse Input FTA MU-TPIX52
MC-TSTX53 HONEYWELL Smart Transmitter Interface FTA for Redundancy MU-TSTX53
MC-TAIH53 HONEYWELL High Level Analog Input FTA MU-TAIH53
Honeywell MC-TSTX13 Smart Transmitter Interface FTA for Redundancy MU-TSTX13
MC-TSTX03 Smart Transmitter Interface FTA MU-TSTX03
Honeywell MC-TAIH13 High Level Analog Input FTA for Redundancy MU-TAIH13
MC-TAIH03 High Level Analog Input FTA MU-TAIH03
Honeywell MC-TAIH12 High Level Analog Input MU-TAIH12
MC-TAIH02 High Level Analog Input MU-TAIH02
Honeywell MU-KFTA05 FTA I/O Cable 5M
MC-ILDX03 Long Distance I/O Link Extender Pair MU-ILDX03
Honeywell MC-IOLX02 I/O Link Extender Pair−Remote Location MU-IOLX02
MC-IOLM02 I/O Link Extender Pair−Main Location MU-IOLM02 51304419-150
Honeywell MU-PFPX01 Blank Filler Plate for I/O Slot
Honeywell MC-PDOY22 Digital Output 32 Processor MU-PDOY22 80363975-150
Honeywell MC-PDOX02 Digital Output Processor MU-PDOX02
Honeywell MC-PDIY22 Digital Input 24 Vdc Processor 80363972-150 MU-PDIY22
Honeywell MC-PDIS12 Digital input processor MU-PDIS12
Honeywell MC-PDIX02 Digital Input Processor MU-PDIX02
Honeywell MC-PAOX03 Analog Output Processor 80363969-150
Honeywell MC-PAOX03 Analog Output Processor
MC-TAIH52 HONEYWELL High Level Analog Input/STI FTA
honeywell MC-PPIX02 Pulse Input Processor (8 Inputs) MU-PPIX02
honeywell MC-PRHM01Remote Hardened Multiplexer IOP MU-PRHM01
honeywell MC-PAIL02 Analog input processor 51304362-350
honeywell MC-PLAM02 Analog Input Multiplexer Processor MU-PLAM02
Honeywell MC-PSIM11 Serial Interface Processor (16 Points/Port) 51304362-350
honeywell MC-PSDX02 51304362-250 Output 8-Point Processor MU-PSDX02
honeywell Processor 16 Inputs MC-PSTX03 51304516-250
honeywell MC-PDIX02 51304362-150 Analog input module
SAIA PCD3.W315 Number of inputs (channels)
DELTA TAU PMAC-2-ACC8T Control board
Automotion ALC12DE-010-1312 Automatic motion servo amplifier
PTM PSMU-350-3 Drive sensor
PROSOFT MVI56E-MNET Network interface module MVI56-PDPS
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