Description
DS200FCRLG1A Boards & Turbine Control 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.
3 Case Studies on Reducing Scrap Rates
Any product assembled or produced in a factory goes through a series of quality tests to determine whether it needs to be scrapped.
High scrap rates are caused by the opportunity cost of not delivering products to customers in a timely manner, wasted personnel time, wasted
non-reusable parts, and equipment overhead expenses. Reducing scrap rates is one of the main issues manufacturers need to address. Ways to
reduce scrap include identifying the root causes of low product quality.
3.1 Data processing
Root cause analysis begins by integrating all available data on the production line. Assembly lines, workstations, and machines make up the industrial
production unit and can be considered equivalent to IoT sensor networks. During the manufacturing process, information about process status,
machine status, tools and components is constantly transferred and stored. The volume, scale, and frequency of factory production considered in
this case study necessitated the use of a big data tool stack similar to the one shown in Figure 2 for streaming, storing, preprocessing, and
connecting data. This data pipeline helps build machine learning models on batch historical data and streaming real-time data. While batch
data analytics helps identify issues in the manufacturing process, streaming data analytics gives factory engineers regular access to the latest
issues and their root causes. Use Kafka (https://kafka.apache.org) and Spark streaming (http://spark.apache.org/streaming) to transmit real-time
data from different data sources; use Hadoo (http://hadoop.apache.org ) and HBase (https://hbase.apache.org) to store data efficiently; use
Spark (http://spark.apache.org) and MapReduce framework to analyze data. The two main reasons to use these tools are their availability as open
source products, and their large and active developer network through which these tools are constantly updated.
ABB 3BHB003152P201 3BHB003152P104
ABB GVC700AE01 3BHB003152P104
ABB GVC700AE01 3BHB003152P201
ABB 3BHB004027R0101 3BHB003152P104
ABB 3BHB004027R0101 3BHB003152P201
ABB 3BHB004027R0101 GVC700AE01
3BHB004027R0101 GVC700AE01 3BHB003152P104
3BHB004027R0101 GVC700AE01 3BHB003152P201
3BHB004027R0101 GVC700AE01 3BHB003152P201 3BHB003152P104
ABB 3BHB005171R0101
ABB CVC750AE101
ABB CVC750AE101 3BHB005171R0101
ABB 3BHE027632R0101
ABB DDC779BE02
ABB 3BHE006805R0002
ABB 3BHE006805R0002 DDC779BE02
ABB GVC736BE101
ABB 3BHE019719R0101
ABB 3BHE019719R0101 GVC736BE101
ABB 3BHE039204P106
ABB 3BHE036204P201
ABB GVC736CE101
ABB 3BHE039203R0101
ABB 3BHE036204P201 3BHE039204P106
GVC736CE101 3BHE036204P201 3BHE039204P106
ABB GVC736CE101 3BHE039204P106
ABB GVC736CE101 3BHE036204P201
3BHE039203R0101 GVC736CE101 3BHE036204P201 3BHE039204P106
3BHE039203R0101 GVC736CE101 3BHE036204P201
ABB 3BHE039203R0101 3BHE039204P106
ABB 3BHE039203R0101 3BHE036204P201
ABB 3BHE039203R0101 GVC736CE101
ABB FPX86-9345–B HL000986P0006
ABB 3BHL000986P0006
ABB LXN1604-6
ABB 3BHL000986P7000
ABB 3BHL000986P7000 LXN1604-6
ABB 3BHL000986P7001
ABB 3BHT300005R1
ABB 3BHE043576R0011
ABB UNITROL 1005-0011 ECO
ABB UNITROL 1005-0011 ECO 3BHE043576R0011
ABB UNS0121A-Z,V1
ABB 3BHE035301R1002
ABB automatic voltage regulator UNITROL 1010
ABB 3BHE035301R1002 UNS0121A-Z,V1
ABB UNITROL 1010 UNS0121A-Z,V1
ABB UNITROL 1010 3BHE035301R1002
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