Description
VMIVME-7750 VMIC series reflective memory card GE 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.
ICS Triplex T9832 Analogue Input Modules
ICS Triplex T8440B Rockwell Module
ICS Triplex T9300 I/O Base Unit
ICS Triplex T9802 Digital Input Module
ICS Triplex T8403 Trusted TMR 24Vdc Digital Input Module
ICS Triplex T8130 Analog Input Modules
ICS Triplex T8442 TMR Speed Monitor Module
ICS Triplex T8193 I/O Communications Module
ICS Triplex T8440B Rockwell Module
ICS Triplex T8193 Controller module
ICS Triplex T8311 Trusted TMR Expander Interface
ICS Triplex T8151B Trusted Communications Interface
ICS Triplex T8442 TMR Speed Monitor
ICS Triplex T8130 Digital Output Module
ICS Triplex T8403 Trusted TMR 24Vdc Digital Input Module
ICS Triplex T8431 Trusted TMR 24Vdc Analogue Input Module
ICS Triplex T8461 TMR 24/48 Vdc Digital Output Module
ICS Triplex T8480 Trusted TMR Analogue Output Module
ICS Triplex T8191 I/O and communication module
ICS Triplex T9402 Digital input module
ICS Triplex T8850 40 channel Analogue or Digital Output
ICS Triplex T8110B Trusted TMR Processor
ICS TRIPLEX T8110 Processor modules trust TMR
ICS TRIPLEX T7310 Module transceiver I/O
ICS TRIPLEX T7484 Module monitoring protection digital output 90/130vac
ICS TRIPLEX T8403 Input module
ICS TRIPLEX T8151B/H Trusted Communication Interface Module
UNS0867A-P,V2 HIEE405246R0002 Excitation system
RELIANCE F-4030-O-H00AA Brushless servo motor
ALSTOM DFI-150-0003 Flame indicator
URRELEKTRONIK 7000-40281-6350300
URRELEKTRONIK 7000-40281-6350150
URRELEKTRONIK 7000-40261-6341500
Bently Nevada 330500-02-CN Piezo-Velocity Sensor
URRELEKTRONIK 8000-88450-000 0000 EXACT12, 8XM12, 4 POLE CAP, PLUG. SCREW-TERM
AFC094AE02 HIEE200130R0002 ARCnet control panel AF C094 AE02
EPICII ALSTOM V4550220-EN Control the dust collector
3500/40M 176449-01 Proximitor Monitor
EATON C825KN10 200A 600V Contactor Ser A2 120V
1762-IF4 Analog input module
FC-PSU-240516 Power Supply Module
3500/42M 140734-02 Proximitor Seismic Monitor
FOXBORO P0973JX Industrial switch
1440-DYN02-01RJ Dynamic measurement module
GESAS CAN-DPV 1168411 AMPLIFIER MODULE
FBM201D Analog Input Interface Modules
MSK030C-0900-NN-M1-UG1-NNNN R911308684 motor
1746-NI4 four-channel analog input module
Allen-Bradley 1746-NO4I Analog output module
1747-L552 B/C CPU MODULE
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