Description
hardware flow control. It is an ideal choice in the field of industrial automation.
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.
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5SHX1445H0001 5SXE05-0152 ABB
5SHX1445H0001 3BHB003230R0101 ABB
5SHX1445H0001 3BHL000391P0101 3BHB003230R0101
5SHX1445H0001 3BHL000391P0101 ABB
5SXE05-0152 IGCT module ABB
3BHB003230R0101 IGCT module ABB
3BHL000391P0101 IGCT module ABB
5SHX1445H0001 IGCT module ABB
3BHL000387P0101 IGCT module ABB
5SHX1445H0002 IGCT module ABB
5SHX1445H0002 3BHL000387P0101 ABB
5SHX14H4502 3BHB003230R0101 5SXE05-0152 3BHB003023P201AEND:B
5SHX14H4502 3BHB003230R0101 5SXE05-0152
5SHX14H4502 5SXE05-0152 ABB
5SHX14H4502 3BHB003023P201AEND:B
5SHX14H4502 3BHB003230R0101 ABB
3BHB003023P201 AEND:B IGCT module
3BHB003230R0101 IGCT module ABB
5SHX14H4502 IGCT module ABB
3BHL000390P0104 IGCT module ABB
5SHX1960L0004 IGCT module ABB
5SHX1960L0004 3BHL000390P0104 ABB
5SHX2645L0004 5SXE04-0150 GVC707AE01
5SHX2645L0004 3BHB003154R0101 ABB
5SHX2645L0004 3BHL000389P0104 3BHB003154R0101
5SHX2645L0004 3BHL000389P0104 ABB
5SXE04-0150 GVC707AE01 ABB
3BHB003154R0101 IGCT module ABB
3BHL000389P0104 IGCT module ABB
5SHX2645L0004 IGCT module ABB
336A4954ARP1 IGCT module ABB
5SHY3545L0003 IGCT module ABB
5SHY3545L0003 336A4954ARP1 ABB
5SHY3545L0005 336A4954ARP2 ABB
336A4954ARP2 IGCT module ABB
5SHY3545L0005 IGCT module ABB
GVC750BE101 IGCT module ABB
3BHE009681R0101 IGCT module ABB
3BHB013085R0001 IGCT module ABB
5SHY3545L0009 IGCT module ABB
5SHY3545L0009 GVC750BE101 ABB
5SHY3545L0009 3BHE009681R0101 ABB
5SHY3545L0009 3BHB013085R0001 ABB
5SHY3545L0009 3BHB013085R0001 3BHE009681R0101
5SHY3545L0009 3BHB013085R0001 3BHE009681R0101 GVC750BE101
5SHY3545L0010 GVC750BE101 ABB
5SHY3545L0010 3BHE009681R0101 ABB
5SHY3545L0010 3BHE009681R0101 GVC750BE101
5SHY3545L0010 3BHB013088R0001 3BHE009681R0101 GVC750BE101
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