Description
F650BFBF1G0HI GE Fanuc Controller Carrie
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.
ABB 3BHB003152P201 3BHB003152P104
ABB GVC700AE01 3BHB003152P104
ABB GVC700AE01 3BHB003152P201
ABB 3BHB004027R0101 3BHB003152P104
ABB 3BHB004027R0101 3BHB003152P201
ABB 3BHB004027R0101 GVC700AE01
3BHB004027R0101 GVC700AE01 3BHB003152P104
3BHB004027R0101 GVC700AE01 3BHB003152P201
3BHB004027R0101 GVC700AE01 3BHB003152P201 3BHB003152P104
ABB 3BHB005171R0101
ABB CVC750AE101
ABB CVC750AE101 3BHB005171R0101
ABB 3BHE027632R0101
ABB DDC779BE02
ABB 3BHE006805R0002
ABB 3BHE006805R0002 DDC779BE02
ABB GVC736BE101
ABB 3BHE019719R0101
ABB 3BHE019719R0101 GVC736BE101
ABB 3BHE039204P106
ABB 3BHE036204P201
ABB GVC736CE101
ABB 3BHE039203R0101
ABB 3BHE036204P201 3BHE039204P106
GVC736CE101 3BHE036204P201 3BHE039204P106
ABB GVC736CE101 3BHE039204P106
ABB GVC736CE101 3BHE036204P201
3BHE039203R0101 GVC736CE101 3BHE036204P201 3BHE039204P106
3BHE039203R0101 GVC736CE101 3BHE036204P201
ABB 3BHE039203R0101 3BHE039204P106
ABB 3BHE039203R0101 3BHE036204P201
ABB 3BHE039203R0101 GVC736CE101
ABB FPX86-9345–B HL000986P0006
ABB 3BHL000986P0006
ABB LXN1604-6
ABB 3BHL000986P7000
ABB 3BHL000986P7000 LXN1604-6
ABB 3BHL000986P7001
ABB 3BHT300005R1
ABB 3BHE043576R0011
ABB UNITROL 1005-0011 ECO
ABB UNITROL 1005-0011 ECO 3BHE043576R0011
ABB UNS0121A-Z,V1
ABB 3BHE035301R1002
ABB automatic voltage regulator UNITROL 1010
ABB 3BHE035301R1002 UNS0121A-Z,V1
ABB UNITROL 1010 UNS0121A-Z,V1
ABB UNITROL 1010 3BHE035301R1002
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