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.
UN 0810A-P Var.2 HIER 448740 R2 Unitrol UN0810A-P module
UN 0807C-P Var.1 HIER 460271 R1 UNITROL
UN 0807B-P Var.1 Unitrol module
UN 0802A-P Var.1 HIER 452076 R1 UNITROL
UN 0803A-P VAR.1 HIER 449240 UNITROL ABB
UN 0801A-P Var.1 HIER 448503 R1 module ABB UNITROL
UN 0800A-P VAR.1 HEIR448194 ABB UNITROL excitation
UN 0720a-P Var.1 HIER443950PI ABB UNITROL module
UN 07209-P Var.1 HIER448877 ABB UNITROL module
UN 0712a-P Var.1 HIER448888R1 ABB UNITROL module
UN 0711b-P Var.1 HIER448871R2 ABB UNITROL module
UN 0710b-P Var.1 HIER449696R1 ABB UNITROL module
UN 0701b Var.1 HIER452221R1 ABB UNITROL module
UN 0700b-P Var.1 HIER449248R1 ABB UNITROL module
871TM-BH8N18-N3 Inductive sensor
UN 0510d-P Var.2 HIER 319619 R2 pulse control module
UN 0503a-P Var.1 HEIR448075R0001 ABB UNITROL module
UN 0503B-P Var.1 HEIR 448075 R2 ABB UNITROL module
UN 0094a-P V.1 HEIR443259R1 test module
UN 0089a-P V.1 HEIR445991R0001 Adapter Print Module
UN 0090a-P V.1 HEIR445926R0001 Sum module ABB UNITROL
UN 0084b-P Var. 1 HEIR445543R0001 Diode Module
106M1081-01 Universal AC Power Input Module
UN 0077a-P V.1 HEIR318418R0001 ABB UNITROL summary and monitoring module
UN 0074a-P V.1HEIR440820R0001 Output signal limiter module
UN 0056a-P V.1 HEIR319429R0001 pulse terminal monitoring module
UN 0053c-P V1 HEIR318757R0001 pulse monitoring module
UN 0040B-P HEIR318790R1 pulse comparator module
UN 0031a-P V.1 HEIR319404R0001 power signal converter module
UN0026 HEIR443243R1 UN0026 Module
UN 0025a-P V.1 HEIR441987R0001 Current indicator module
UN 0006a-P V.1 EIR315104R1 Voltage regulator module
UN 0004a-P V.1 HEIR 316364 R1 pulse intermediate stage module UN0004a-P V.1
KX 9190a HIEE320466R1 Excitation system
HIEE220280R11 KU7461d ABB module Excitation regulator
UNS0980c-P V3 HIEE405205 R3
UNS0982b-P V1 HIEE405087R1 ABB UNITROL series
UNC4672a-P, V1 HIEE205012R0001 Measuring Interface
HIEE205014R0001 UNC 4673A,V.1 Measuring Interface Uni
3BSE007836R1 DSTYW121 Voltage Transformer Unit
SS110 3BSE007698R1 Voting Unit DC 24V
UVC 691 204-691-000-021 Vibration Processor Module
UVC 752 204-752-000-014 Vibration Processor Module
PLD 772 254-772-00-212 Vibro-Meter Digtal Display Module
PAA758 254-758-000-104 Vibro-Meter Indicator Module
GSI 130 Galvanic Separation Unit
APF 184 204-000-012 Vibro-Meter Power Supply Module
VIBRO-METER APF 160 Power Supply
ACP006 Control Panel Module Vibro-Meter
ACM 215 VIBRO-METER 204-215-000-101 Module
ABF 160 Power Supply Module Vibro-Meter
A6500-TP Temperature / Process Card
204-007-000-102/5174 Vibro-Meter U/I Module
204-007-000-102 Vibro-Meter U/I Module
3500/15-05-05-05 Ac and DC power modules
CP103 piezoelectric pressure sensor Vibro-Meter
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