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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S70601-SE S700 Servo Drive
S70601-NA S700 series
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S70302-PBNANA S700 Servo Drive
S70302-NANANA-NA S700 Servo Drive
S70302-NANANA S700 Servo Drive Series
S70301-NANANA S700 Servo Drive
KOLLMORGEN S70301-NA S700 Servo Drive Series
S70102-NANANA KOLLMORGEN S700 Servo Drive Series
S70101-NA KOLLMORGEN
S70602-PBNANA KOLLMORGEN driver
NFAI841-S00 YOKOGAWA plc module
ALSTOM N897164624A SDTC Tx Rx Module F14-F7
ALSTOM N897164611M TARJETA TX/RX F7 P/RACK D/ARMARIO SDTC
ALSTOM N897164610L
ALSTOM N895314513L
ALSTOM N895313512X
ALSTOM N70032702L Shibata-type automatic coupler
IMASI23 16-Channel Universal Analog Input Module
ICS TRIPLEX T8403 Trusted TMR 24 Vdc Digital module
ETT-VGA-0045 10” 4 TFT color graphic touch screen
DSPC174 Processor Board 3BSE005461R1
CP310250 KOLLMORGEN server Driver
CML40.2-SP-330-NA-NNNN-NW Drive Controller
133396-01 Overspeed Detection I/O Module
125840-01 High Voltage AC Power Input Module (PIM)
UN0901DV1 HIER460279R1 ABB module
UN-0901b-P Var.1 HEIR 319360R1 Unitrol UN-0901b-P Module
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UN 0911b-P HEIR448648R0001 Voltage matching module
UN 0910a-P Var.1 HEIR444516R0001 voltage monitoring module
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UN 0906b-P HIER448845R0001 Circuit breaker Time selection module
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UN 0900B-P Var.1 HEIR319360R1, UN 0900b-P V1 DC/DC Power module
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