Description
IC6RTB-01C-SA01 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.
NF93A-2 HESG440280R2 ABB | Power electronic module
NDCU-12C NDCU-12CK ABB | Analog input subroutine
MSR04X1 ABB | DCS power module
MC91 HESG440588R4 HESG112714/B ABB | Pulse amplifier plate
3BHL000986P7000 ABB | Processor end module
SG13433241070 ABB | Modular redundant controller
GJR5252300R3101 ABB | Communication control panel
3BHE046836R0101 ABB | Controller system in stock
3BHE022294R0103 ABB | Central processing unit
3BHE022294R0103 ABB | Processor unit
PPD113-B03-23-111615 ABB | Central processing unit
PP835 3BSE042234R1 | Touch screen
ABB PP825A 3BSE042240R3 | Touch screen
PP815A 3BSE042239R2 ABB | Man-machine interface
PP836 3BSE043449R501 ABB | Man-machine interface
PP820 3BSE042243R1 ABB | Function key panel
TRICONEX 3805E | Signal input/output module
PP826 3BSE042244R1 ABB | Function key panel
SCHNEIDER TSXCUSBMBP | PLC control system
RELIANCE ELECTRIC WR-D4008 | Digital input module
RELIANCE ELECTRIC S-D4043 | Distributed control system
RELIANCE ELECTRIC S-D4041 | Programmable control system
RELIANCE ELECTRIC S-D4012 | Channel analog input
RELIANCE ELECTRIC S-D4007 | Programmable control system
RELIANCE ELECTRIC S-D4006 | Multifunctional controller
NI SCXI-1520 | strain input module
NI SCXI-1127 | Multiplexer switch module
NI SCXI-1125 | Voltage input module
NI SCXI-1000 | SCXI Chassis NI
LAETUS LLS570-05 | Laser scanner
GE WESDAC D20ME | Digital input module
GE WES5302-150 | I/O communication modules
GE WES5162 | frame
GE WES5302-111 | Distributed control system
GE WES5162-9101 | Frame interface module
GE WES5123-2600 |Analog output module
WES5123-1200 GE | Analog input module
WES5120 5120-1106 GE | Serial communication module
WES5120 5120-1506 GE |Field controller master unit
PRG-MODEM GE |8-channel digital input
WES13-3 GE |Pressure regulating device main control board
DL-1200/RTU GE | Digital input module |New spot
GE D20 MIC 10BASE-T |I/O module|Fresh spot
Framework of GE D20 EME | Fresh spot
GE D20 EME 10BASE-T | I/O unit module |New spot
D20 EME GE | Analog input module | Fresh spot
XV-440-10TVB-1-13-1 EATON | Man-machine interface |New spot
PU512V2 3BUR001401R1 ABB | Digital input card |New spot
1TGE120010R1300 ABB | Digital input card | Fresh spot
REG216 | Robot drive power supply | ABB
216NG63 | Analog input board | ABB
S-D4041 | Analog input module | RELIANCE ELECTRIC
S-D4043 | PLC controller | RELIANCE ELECTRIC
WR-D4008 | Analog input module | RELIANCE ELECTRIC
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