Description
IS220PAOCH1BD Exciter terminal board
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.
Display operation panel OCAH 940181103
Display operation panel O3EX HENF315845R2
Display operation panel O3ES HENF445789R1
Display operation panel O3EId HENF452777R3
Display operation panel O3EHa HENF315087R2
Display operation panel O3EGb HENF315118R2
Display operation panel O3EEb HENF318176R1
Display operation panel O3ED
Display operation panel O3EC HENF442581R1
Display operation panel NXE100-1608SB
Display operation panel NXE1001-608DBW
Display operation panel NWX511a-2/R HESG112548R12
Display operation panel NUIM-62C
Display operation panel NU8976A99 HIER466665R0099 HIEE320693R0001
Display operation panel NU8976A
Display operation panel NU8976A
Display operation panel NTU-7U0
Display operation panel NTU-7Q2E
Display operation panel NTU7Q2E
Display operation panel NTU-7Q2
Display operation panel NTU-7Q1
Display operation panel NTU-7L0
Display operation panel NTU-7I6/48S1
Display operation panel NTU-7I6/24S1
Display operation panel NTU-7I4
Display operation panel NTU-7I1
Display operation panel NTU-7I0
Display operation panel NTU-7G7
Display operation panel NTU-7C9
Display operation panel NTU-7C7
Display operation panel NTU-7C6/MO
Display operation panel NTU-7C2
Display operation panel NTU-7C1
Display operation panel NTU-7B2/A
Display operation panel NTU-7B1/A
Display operation panel NTU-7B1
Display operation panel NTU-7B0
Display operation panel NTU-7A9/M1
Display operation panel NTU-7A9/M0
Display operation panel NTU-7A4/20MA
Display operation panel NTU-7A0/P
Display operation panel NTU-7A0/E
Display operation panel NTU-7A0
Display operation panel NTU-738A
Display operation panel NTU-716/48S3
Display operation panel NTU-716
Display operation panel NTU-715
Display operation panel NTU-7/6/48S1
Display operation panel NTTA01
Display operation panel NTST01
Display operation panel NTST01
Display operation panel NTSM01
Display operation panel NTSE01
Display operation panel NTRO02-A
Display operation panel NTRL03
Display operation panel NTRL02B
Display operation panel NTRL02A
Display operation panel NTRL01
Display operation panel NTR002-A
Display operation panel NTR002-A
Display operation panel NTPL01
Display operation panel NTMU02
Display operation panel NTMU01
Display operation panel NTMP01
Display operation panel NTMP01
Display operation panel NTMF01
Display operation panel NTMF01
Display operation panel NTLS01
Display operation panel NTLS01
Display operation panel NTHS03
Display operation panel NTHS03
Display operation panel NTHS03
Display operation panel NTFB01
Display operation panel NTDRO01
Display operation panel NTDO02
Display operation panel NTDO01
Display operation panel NTDI21-A
Display operation panel NTDI21-A
Display operation panel NTDI02
Display operation panel NTDI01
Display operation panel NTDI01
Display operation panel NTDI01
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