Description
IC698CPE030-BA 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.
3HAC020466-001 Robot spare parts ABB
DSQC627 Robot spare parts ABB
DSQC626 Robot spare parts ABB
DSQC609 Robot spare parts ABB
DSQC608 Robot spare parts ABB
DSQC602 Robot spare parts ABB
3HAC5687-1/06 Robot spare parts ABB
DSQC509 Robot spare parts ABB
DSQC509 3HAC5687-1/06 ABB
DSQC504 3HAC5689-1/04 ABB
3HAC5689-1/04 Robot spare parts ABB
DSQC504 Robot spare parts ABB
DSQC355A Robot spare parts ABB
DSQC354 Robot spare parts ABB
DSQC352 Robot spare parts ABB
DSQC346G Analog output board ABB
DSQC346B 3HAB8101-6/11A ABB
3HAB8101-6/11A Analog output board ABB
DSQC346B Analog output board ABB
DSQC327A Analog output board ABB
DSQC332A 3HAC17973-1/02 ABB
3HAC17973-1/02 Analog output board ABB
DSQC332A Analog output board ABB
DSQC322 Analog output board ABB
DSQC313 Analog output board ABB
DSQC202 Analog output board ABB
57360001-HG Analog output board ABB
DSMB127 Analog output board ABB
DSMB127 57360001-HG ABB
DSMB-02C 3AFE64666606 ABB
3AFE64666606 ABB Analog output board
DSMB-02C ABB Analog output board
DSIH72VPENOK ABB Analog output board
DSDX453 5716075-AN ABB
5716075-AN Analog output board ABB
DSDX453 Analog output board ABB
5716075-P Analog output board ABB
DSDX452 Analog output board ABB
DSDX452 5716075-P ABB
DSDX404 57160001-TE ABB
57160001-TE Analog output board ABB
DSDX404 Analog output board ABB
57160001-ADF Analog output board ABB
DSDP170 Analog output board ABB
DSDP170 57160001-ADF ABB
DSDP150 57160001-GF ABB
57160001-GF Analog output board ABB
DSDP150 Analog output board ABB
57160001-ACX Analog output board ABB
DSDP140B Analog output board ABB
DSDP140B 57160001-ACX ABB
DSDP140A 57160001-ACT ABB
57160001-ACT Analog output board ABB
DSDP140A Analog output board ABB
3BSE018298R1 Analog output board ABB
DSDO115A Analog output board ABB
DSDO115A 3BSE018298R1 ABB
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