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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Vibro-meter VM600-ABE040 204-040-100-011 system racks
IS200EROCH1AED digital Signal processor control panel
IS200ERIOH1ACB excitation regulator I/O board
900CS10-00 Touch Screen
PLX31-EIP-MBS4 Ethernet /IP to Modbus Serial 4 ports
Honeywell 9211-ET-HN1 51154724-100 MODBUS/TCP firewall
VMIVME7750-734 VME printed circuit board
VME-7807RC-414001 single board computer
CC-TDOR01 digital output relay module
DC-TDOB01 51307186-275 Digital output module
S70602-NANANA digital servo driver
PDC235 3BHE032025R0101 Unitrol PC D235 exciter control module
3500/15-04-01 3500/15 AC power supply
3500/42-02-R0 Proximitor seismic monitor
3500/42-09-01 Proximitor earthquake monitor
3500/04-01 3500/04 Internal barrier grounding module
3500/42-09-00 Proximitor seismic monitor 3500/42M
KJ4001X1-BE1 Input/output carrier
VM600 IOCN 200-566-000-012 200-566-101-012 input/output card
3500/92-04-01-00 3500/92 Communication gateway
KJ3102X1-BA1 Security simulation input card
MVME177-003 single board computer
IC695PSA040F RX3i Power module
IS200TBCIH21CD Contact input terminal board
3500/53M-03-00 3500/53M Electronic Speed detection system
MVME162PA-252SE Embedded controller
8102-HO-IP GE 8-channel Analog Output
DEIF RMP-112D Power relay
DEIF GPU-3 generator protection unit
AAI543-H50 Analog output module
369-HI-R-M-0-0-H-E Series 369 motor management relay
IC660BRD025 Genius Modular redundant receive and output module
DSQC633C 3HAC043904-001/06 Measuring unit
2711P-T12C4A6 Man-machine interface (HMI) device
MSM031B-0300-NN-M0-CH0 servo motor
IC693CMM302L Genius communication module
140CPU42402 CPU interface support module
6KAVI43030Y1B2 GE 300i 30HP INVERTER DRIVE
5464-834 Speed sensor WOODWARD 5000 Modue Rev.A
5202-MNET-MCM4 ProLinx EtherNet/IP to Modbus Primary/secondary gateway
SSB401-53 ESB Bus Interface Slave Module
SAI143-H53 S2 Analog Input Module
PM571-ETH-V14x 1SAP130100R0270 Logic controller
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