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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Vibro-meter 204-040-100-011
VM600-ABE040 204-040-100-011
Vibro-meter 204-042-100-011
Vibro-meter VM600-ABE042
VM600-ABE042 204-042-100-011
VIBRO-METER 444-680-000-511
VIBRO-METER 573-935-202C
VIBRO-METER VM600 XMV16
VIBRO-METER 600-003 620-001-001-116
600-003 620-001-001-116 VM600 XMV16
VIBRO-METER VM600 XIO16T
VIBRO-METER 620-003-111-112
VIBRO-METER 620-002-000-113
VIBRO-METER 620-002-000-113 620-003-111-112
620-002-000-113 620-003-111-112 VM600 XIO16T
VIBRO-METER IOCN 200-566-000-112
B&K Vibro VB-430 C002292.01/9100131600
B&K Vibro VB-430
Vibro-meter VM600 MPC4
Vibro-meter 200-510-111-013
Vibro-meter 200-510-017-019
Vibro-meter 200-510-017-019 200-510-111-013
200-510-017-019 200-510-111-013 VM600 MPC4
Vibro-meter 200-560-101-015
Vibro-meter 200-560-000-018
Vibro-meter 200-570-101-013
Vibro-meter 200-570-000-014
Vibro-meter VM600
200-570-000-014 200-570-101-013 VM600
VIBRO-METER VM600 IOC4T
VIBRO-METER 200-560-000-018 200-560-101-015 VM600 IOC4T
HIMA F8620/11
HIMA BV7032-0,5
HIMA BV7046-4
HIMA EABT3 B9302 997009302
HIMA ELOPII
HIMA F3 AIO 8/4 01
HIMA F3 DIO 20/8 02
HIMA F3 DIO 8/8 01
HIMA F3231
HIMA F3236
HIMA F3237 984323702
HIMA F3322
HIMA F3330 984333002
GE IP-QUADRATURE
ABB CPU PCD237
ABB CPU PCD287
HIMA F35
HIMA F6214
HIMA F6215
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