Description
DS200TCCBG3BDC GE Steam Turbine System
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.
3BHB030310R0001 IGCT module ABB
5SHY4045L0006 IGCT module ABB
5SXE08-0167 IGCT module ABB
AC10272001R0101 IGCT module ABB
5SHY5045L0020 IGCT module ABB
5SHY5045L0020 AC10272001R0101 ABB
5SHY5045L0020 5SXE08-0167 ABB
5SHY5045L0020 5SXE08-0167 AC10272001R0101
5SHY5055L0002 3BHE019719R0101 GVC736BE101
5SHY5055L0002 GVC736BE101 ABB
5SHY5055L0002 3BHE019719R0101
GVC736BE101 IGCT module ABB
3BHE019719R0101 IGCT module ABB
5SHY5055L0002 IGCT module ABB
5SXE10-0181 IGCT module ABB
AC10272001R0101 IGCT module ABB
5SHY6545L0001 IGCT module ABB
5SHY6545L0001 5SXE10-0181 ABB
5SHY6545L0001 AC10272001R0101
5SHY6545L0001 AC10272001R0101 5SXE10-0181
3BHE024855R0101 UFC921A101 ABB
3BHE024855R0101 Industrial module ABB
UFC921A101 Industrial module ABB
HIEE300910R1 Industrial module ABB
UFC092BE01 Industrial module ABB
UFC092BE01 HIEE300910R1 ABB
UFC718AE101 HIEE300936R0101 HIEE410516P201AENDE
UFC718AE101 HIEE410516P201AENDE
UFC718AE101 HIEE300936R0101 ABB
HIEE410516P201AENDE Main control board
HIEE300936R0101 Main control board ABB
UFC718AE101 Main control board ABB
3BHB000272R0001 Main control board ABB
3BHB003041R0001 Main control board ABB
UFC719AE01 Main control board ABB
UFC719AE01 3BHB000272R0001 ABB
UFC719AE01 3BHB003041R0001 ABB
UFC719AE01 3BHB003041R0001 3BHB000272R0001
UFC721AE 3BHB002916R0001 ABB
3BHB002916R0001 Main control board ABB
UFC721AE Main control board ABB
3BHE021889R0101 Main control board ABB
UFC721BE101 Main control board ABB
UFC721BE101 3BHE021889R0101 ABB
3BHE004573R1142 Main control board ABB
UFC760BE1142 Main control board ABB
UFC760BE1142 3BHE004573R1142 ABB
UFC760BE141 3BHE004573R0141 ABB
3BHE004573R0141 Main control board ABB
UFC760BE141 Main control board ABB
3BHE004573R0142 Main control board ABB
UFC760BE142 Main control board ABB
UFC760BE142 3BHE004573R0142 ABB
UFC760BE41 3BHE004573R0041 ABB
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