Description
4006L6501AAG002 Canopen Interface Module
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.
2 Leveraging big data tool chains
After the data collected from the manufacturing product value chain is stored in the database, a data analysis system is required to analyze the data.
The manufacturing data analysis system framework is shown in Figure 1. Data is first extracted, transformed, and loaded (ETL) from different
databases into a distributed file system, such as Hadoop Distributed File System (HDFS) or a NoSQL database (such as MongoDB). Next,
machine learning and analytics tools perform predictive modeling or descriptive analytics. To deploy predictive models, the previously mentioned tools
are used to convert models trained on historical data into open, encapsulated statistical data mining models and associated metadata called Predictive
Model Markup Language (PMML), and Stored in a scoring engine. New
data from any source is evaluated using models stored in the scoring engine [9].
A big data software stack for manufacturing analytics can be a mix of open source, commercial, and proprietary tools. An example of a
manufacturing analytics software stack is shown in Figure 2. It is known from completed projects that existing stack vendors do not currently
offer complete solutions. Although the technology landscape is evolving rapidly, the best option currently is modularity with a focus on truly distributed
components, with the core idea of success being a mix of open source and commercial components [10].
In addition to the architecture presented here, there are various commercial IoT platforms. These include GE”s Predix ( www.predix.com ), Bosch”s IoT
suite (www.bosch-iot-suite.com), IBM”s Bluemix ( www.ibm.com/cloud-computing/ ), ABB based on Microsoft Azure IoT services and people platform
and Amazon’s IoT cloud (https://aws.amazon.com/iot). These platforms offer many standard services for IoT and analytics, including identity management and data
security, which are not covered in the case study here. On the other hand, the best approaches offer flexibility and customizability, making implementation
more efficient than standard commercial solutions. But implementing such a solution may require a capable data science team at the implementation site.
The choice comes down to several factors, non-functional requirements, cost, IoT and analytics.
IS200EBKPG1B exciter backplane control board
IS200EBKPG1A Excitation control backplane
IS200EBACG1A Digital controller
IS200EAUXH1A digital controller
IS200EACFG3A feedback board
IS200EACFG3B exciter AC feedback board
IS200EACFG2B excitation control module
IS200EACFG2A actuator AC feedback board
IS200EACFG1B exciter module
IS200EACFG1A AC feedback board
IS200DSPXH2D processor control board
IS200DSPXH1D digital signal processor control board
IS200DSPXH1C processor controller
IS200DSPXH1B digital signal processor board
IS200DSPXH1A digital signal controller
DS2020DACAG2 EX2100 series power module
DS2020DACAG1 Power module
IS420YDOAS1B I/O Ethernet network
IS420YAICS1B simulates I/O
IS420UCSDH1A quad-core controller
IS420UCSCH1B Balance controller
IS420UCSCH1C Mark VIe controller
Four core controller IS420UCSCH1A
IS420UCSBS1A Security controller
IS420UCSBH4A high efficiency controller
IS420UCSBH3A Mark VIe controller
IS420UCSBH1A high-speed controller
IS420UCPAH2A extended I/O board
IS420PUAAH1A I/O module
IS420PSCAH1B Communication I/O module
IS420PPNGH1A controller
IS420PFFAHIA bus gateway
IS420PFFAH1A Gateway
IS420ESWBH5A Control system module
IS420ESWBH4A 16-port gateway module
IS420ESWBH3A 16 Port module GE IS420ESWBH2A
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