Description
IS420UCSCH1B General Electric Processor Board Mark VI
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.
61C22A | Reliance | Local I/O Head
SARCR-XFB01A01 | YASKAWA | Device-Net
5A26458G05 | EMERSON | Relay output card
1SVR040000R1700 | ABB | CC-U/STD Universal Signal Converter
MVME2604 712-IO | MOTOROLA | VME module
20BC2P1A0AYNANC0 | Allen-Bradley | PowerFlex 70 series drive
TU846 3BSE022460R1 | ABB | module termination unit
TC514V2 | ABB | TC514V2 AF 100 Twisted pair/opto modem
TU842 | ABB | TU842 Redundant MTU, 50V
UNITROL1000 Z.V3 3BHE014557R0003 | ABB | Excitation systems
SS832 | ABB | Voting Units
REX521GHHPSH50G | ABB | PROTECTION UNIT
SR489-P1-LO-A20-E | GE | Relay motor management
SD834 | ABB | Power Supply Units
RET650 | ABB | Transformer protection
REM545 | ABB | Feeder terminal
REF541KM118AAAA | ABB | FEEDER TERMINAL
PM860 | ABB | AC 800M PM860 CONTROLLER
IC800SSI228RD2 | GE | Servo Motor Controller
IC694MDL655 | GE | 32-Point, 24 VDC, Positive/Negative Logic, Input module
IC694MDL753 | GE | 12 / 24 VDC, High-density Positive Logic discrete output modul
IC200UDR005 | GE | 28-point Micro PLC module
IC693MDL730 | GE | 12/24 Volt DC Positive Logic 2 Amp Output module
FPR3346501R1012 | ABB | ICSE08B5-2 I/O MODULE
F650-B-F-G-F-2-G-1-HI-E | GE | Feeder Protection & Bay Controller
IRDH375 | ISOMETER | Insulation monitoring device
DSTC454 5751017-F | ABB | DSTC 454 Optical Modem for 2 Mbits/s
DSAX452 | ABB | Remote In / Out Basic Unit
AO845A 3BSE045584R1 | ABB | Analog Output Module
469-P5-LO-A20-E | GE | LO Control Power with 4-20mA Analog Outputs
469-P1-HI-A20-E | GE | 469 Motor Management Relay
HC-6002-2 | HUMO LABORATORY | MODULE
HC703BS-E51 | Mitsubishi | Motors-AC Servo
3HAC025562-001 | ABB | DSQC 655 Capacitor Unit
3BDH000320R0101 | ABB | LD 800HSE Linking Device
1VCF752000 | ABB | Feeder terminal
TU810 3BSE013230R1 | ABB | 16 channel 50 V compact module
R911328500 | Rexroth | Servo Drives
MSK050C-0300-NN-M1-UG1-NNNN | Rexroth | MSK Synchronous Motors
IC693CHS391 | GE | expansion plate with ten slots
HMS01.1N-W0070-A-07-NNNN | Rexroth | HMS Single Axis Inverters
DO801 3BSE020510R1 | ABB | 16 channel 24 V digital output module
330930-065-01-05 | Bently Nevada | NSv Extension Cable
CAN-32DO0.5A-P-2X16 | ETON | Digital output module
DI801 3BSE020508R1 | ABB | 16 channel 24 V digital input module
DKC02.3-100-7-FW | Rexroth | EcoDrive Drive Controller
16710-30 | Bently Nevada | 16710 Interconnect Cables
330180-51-05 | Bently Nevada | 3300 XL Proximitor® Sensor
130539-30 | PILZ | Interconnect Cable
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