Description
DS200DSPCH1ADA GE 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.
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.
ABB 3BSE015088R1
ABB PFSK162
ABB PFSK162 3BSE015088R1
ABB 3BSE021180R1
ABB PFSK164
ABB PFSK164 3BSE021180R1
ABB 3BHB004661R0001
ABB KUC711AE
ABB KUC711AE 3BHB004661R0001
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ABB KUC711AE01 3BHB004661R0001
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ABB KUC711AE101 3BHB004661R0101
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ABB KUC720AE
ABB KUC720AE 3BHB000652R0001
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ABB 3BHB003431R0101
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3BHB003431R0101 3BHB000652R0101
KUC720AE101 3BHB000652R0101
KUC720AE101 3BHB003431R0101
KUC720AE101 3BHB003431R0101 3BHB000652R0101
ABB 3BHB005243R0105
ABB KUC755AE105
ABB KUC755AE105 3BHB005243R0105
ABB 3BHB005243R0106
ABB KUC755AE106
ABB KUC755AE106 3BHB005243R0106
ABB 3BHB005243R0117
ABB KUC755AE117
ABB KUC755AE117 3BHB005243R0117
ABB 1MRS050775
ABB SACE750090R0002
ABB 1VCR007346
ABB REF542PLUS 1VCR007346
ABB REF542PLUS
ABB CE446BB1NH
ABB REF601
ABB REF601 CE446BB1NH
ABB 1MRS050775
ABB REF610
ABB REF610 1MRS050775
ABB REF610C11LCLR
ABB REF610C11LCLR 60HZ
ABB HAFAABAAABE1BCA1XE
ABB REF615A_E
ABB REF615A_E HAFAABAAABE1BCA1XE
ABB REF615C
ABB HCFFAEAGANB2BAN1XC
ABB REF615C_C
ABB REF615C_C HCFFAEAGANB2BAN1XC
ABB HCFDACADABC2BAN11E
ABB REF615C_E
ABB REF615C_E HCFDACADABC2BAN11E
ABB HCFDACADABC2BAN11E
ABB REF615C_E
ABB REF615C_E HCFDACADABC2BAN11E
ABB HBFEAEAGABCCANC11E
ABB REF615E_E HBFEAEAGABCCANC11E
ABB HBFHAEAGNCA1BNN1XE
ABB REF615E_E
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