Description
A02B-0236-C231 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.
149992-01 Bently Nevada Spare 16-Channel Relay Output Module
330704-000-050-10-02-05 Bently Nevada Proximity Probes
3500/20 Bently Nevada RACK INTERFACE MODULE
3500/45-01-00 Bently Nevada Position Monitor
3500/40-01-00 Bently Nevada Proximitor Monitor
873EC-JIPFGZ FOXBORO electrochemical analyzer
P-HA-RPS-32200000 ABB Power Supply
3500/50-01-00 Bently Nevada Tachometer Module
3500/45 Bently Nevada Position Monitor
3500/42-01-00 Bently Nevada Displacement monitor
1C31224G01 Westinghouse Analog Input Module
140CPU65150 Schneider Unity processor
3640E TRICONEX Digital Output Modules 24VDC 16 Point TMR
IC697VAL132 GE ANALOG INPUT MODULE
80190-580-51 Allen-Bradley Communication card
DIGIFAS7108 DIGIFAS Driver module
IORE63-20-CC ABB fuse protector
IS200DSPXH1DBC GE printed circuit board
IS200EPSMG2ADC GE Printed Circuit Board
IS200EPSMG2AEC GE Boards Mark VI IS200
IS200ERDDH1ABA GE Input module board
IS200ERIOH1AAA GE Analog Output Module
IS200EROCH1ABB GE Boards Mark VI IS200
IS200TREAH2AED GE Controller Module
MMS6823R EPRO Data acquisition function
PM904F 3BDH001002R0001 ABB AC 900F controller
REF615C-D HCFFAEAGABC2BAA1XD ABB Feeder protection device
REM545BM222BAAA ABB MACHINE TERMINAL
REM615 HCMJAEADABC2BNN11E ABB Motor protection and control
T8231 ICS Triplex Power Pack universal input
UMC100 ABB Intelligent motor management
1HSB495663-2 ABB DENSITY GAUGE
200-560-000-113 VIBRO vibrometer
UNS0881a-P,V1 3BHB006338R0001 ABB GDI PCB completed *PB
3500/53 133388-01 Bently Nevada Overspeed Detection Module
AI830 3BSE040662R1 ABB Analog Input
AI820 3BSE008544R1 ABB Analog Input
AO820 3BSE008546R1 ABB Analog Output
TEAM BL0308 Circuit board module
CI856K01 3BSE026055R1 ABB S100 I/O Interface
CI857 3BSE018144R1 ABB AC 800M controllers
CTI121-P Ex 3BDH000741R1 ABB Controller module
E69F-BI2 FOXBORO Field Mounted Current to Pneumatic Converter
DI803 3BSE022362R1 ABB digital input module
DEIF DU-2MKIII Potection and Power Management multi-line
E69F-BI2-MS FOXBORO Field Mounted Current to Pneumatic Converter
IS200AEPCH1BAA GE energy pitch center module
IS210BPPBH2BMD GE Mark VI printed circuit board
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