Description
hardware flow control. It is an ideal choice in the field of industrial automation.
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.
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A413139 Network I/O processor
DSTA131 Drive data manager controlling drives
PXIE-5105 High-density PXI oscilloscope
D201471 Digital input/output module
A413188 Multiprocessor system
IC200ALG320 Analog output module
D201376 Analog input module analog signals
XVR19 6U-VME-SBC Single board computer
D201190 Flow control valve high performance
CTB810 HN800 Communication terminal board
FPN1903A Switching power supply
CRIO-9073 controller embedded system
D3000 Dual protection mechanism
SHC68-68-EPM With redundant power input
BMXDDI3202K Input module into digital
BMXCPS2010 isolated power module
BMXDDO6402K Discrete quantity output module
1794-IF4IXT Motor control equipment
150-C85NBD Soft starter redundant input
HPC800底座MB810 Data collection and alarm management
A413139 Computer controller
VMIVME 3215-000 cpu module
PC834-001-T laboratory instruments
D201471 Digital input and output module
F860-CA High quality terminal board
MDB-8E Micro electronic balance
AI02J High precision current transmitter
FCM2F2 P0914YZ Bus extender
CRIO-9073 embedded controller
ZMI-4104 displacement measurement electronic
S30361-NA DANAHER servo controller
PW702 Power module overvoltag
REF610C11HCNN01(含底座) contactor
MVME55006E-0161R High performance embedded computer
87199-01 digital signal processor
MTL4842 Surge Protective Device
85UVF1A-1QD Solid state flame detector
EMERSON A6370D Digital expansion module
IC200PBI001 GE Network Interface Unit
CRIO-9073 NI CompactRIO Controller
85UVF1A-1QD Integrated Flame Scanner with Internal Relay
S30361-NA DANAHER KOLLMORGEN Servo driver
ZMI-4104 Displacement measurement Interferometers
PW702 YOKOGAWA Dc power module
“ABB AI02J A current transmitter”
Foxboro RH926GQ Distributed I/O module
FOXBORO FCM2F2 P0914YZ Bus expander
A-B 1756-IF4FXOF2F Analog input channel
VE4006P2 Programmable controller
136703-01 Communication module
ABB DSQC652 Standard IO signal board
EMERSON IC200PBI001 Network interface unit
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