Description
hardware flow control. It is an ideal choice in the field of industrial automation.
3.2 Machine learning
As the functionality of distributed computing tools such as Spark MLLib (http://spark.apache.org/mllib) and SparkR (http://spark.apache
.org/docs/latest/index.html) increases, it becomes It is easier to implement distributed and online machine learning models, such as support
vector machines, gradient boosting trees and decision trees for large amounts of data. Test the impact of different machine parameters and process
measurements on overall product quality, from correlation analysis to analysis of variance and chi-square hypothesis testing to help determine the impact of individual
measurements on product quality. This design trains some classification and regression
models that can distinguish parts that pass quality control from parts that do not. The trained models can be used to infer decision rules. According to the highest purity rule,
purity is defined as Nb/N, where N is the number of products that satisfy the rule and Nb is the total number of defective or bad parts that satisfy the rule.
Although these models can identify linear and nonlinear relationships between variables, they do not represent causal relationships. Causality is critical to
determining the true root cause, using Bayesian causal models to infer causality across all data.
3.3 Visualization
A visualization platform for collecting big data is crucial. The main challenge faced by engineers is not having a clear and comprehensive overview of the complete manufacturing
process. Such an overview will help them make decisions and assess their status before any adverse events occur. Descriptive analytics uses tools such as
Tableau (www.tableau.com) and Microsoft BI (https://powerbi.microsoft.com/en-us) to help achieve this. Descriptive analysis includes many views such as
histograms, bivariate plots, and correlation plots. In addition to visual statistical descriptions,
a clear visual interface should be provided for all predictive models. All measurements affecting specific quality parameters can be visualized and the data
on the backend can be filtered by time.
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1SBP260020R1001 07CR41 ABB Advant Controller Basic Unit
140A-C-ASA11A Allen-Bradley switch
AO610 ABB Analog Output 16Ch
CM400RGICH1ACB GE Automation Module
DI651 ABB Digital Input 32Ch
EP3-E-4-A ALSTOM MICRO CONTROLLER/ PROCESSOR
IB3110250 ELEMASTER
MRP528516 IS200EXHSG3AEC GE printed circuit board
MRP081636 IS200TSVCH1AJE GE
RF615 RC610 ABB I/O station with redundant CI610
UR6EH GE Digital I/O Input Output Module
X1052 DELL switch – 48 ports – Managed – rack-mountable
140DDO84300 Schneider DISCRETE OUTPUT MODULE
1756-BA2 Allen-Bradley ControlLogix Lithium Battery
FBM222 P0926TL FOXBORO Profibus DP Module
IC660TBA026M GE Output block
IC660TBD024K GE DC 32 circuit discrete input and output
IOP351 METSO control system
SB510 3BSE000860R1 ABB Backup Power Supply 110/230V AC
100031593.10 LSU-112DG DEIF load sharing units
R901325866 Rexroth Proportional Valve Solenoid Valve Hydraulic Valve
5600389 T8850 ICS Triplex 5600389 T8850
ZMU-02 ABB FOR DEMO RACK
1FT5066-0AC71-2-Z Siemens Permanent magnet motor
223BD-00001AAB MKS Baratron Pressure Transducer
CB801 3BSE042245R1 ABB Profibus DP Communication Interface
3BHB003688R0001 ABB Module
REM615E_1G ABB FEEDER PROTECTION AND CONTROL RELAY
CS513 3BSE000435R1 ABB 802.3 LAN-Module
136188-01 Bently Nevada Communication Gateaway Module
3500/25 184684-01 Bently Nevada Enhanced Keyphasor Module
3500/05-01-03-00-00-00 Bently Nevada 3500/05 System Rack
3500/44M 176449-03 Bently Nevada Aeroderivitive GT Vibration Monitor
UR8FH GE Universal Relays of the UR Series
3500/32 125712-01 Bently Nevada 4-channel relay module
3500/40M 140734-01 Bently Nevada Proximitor monitor I / O module
3500/40M 176449-01 Bently Nevada Proximitor Monitor
3500/42M 176449-02 Bently Nevada Proximitor/Seismic Monitor
125680-01 Bently Nevada Proximitor I/O Module
CE4003S2B6 Emerson Standard I/O Termination Block
F8620/11 HIMA CPU Module
FS7-2173-2RP HONEYWELL FS7 Multispectral Fire and Flame Detector
IC698PSA100D GE power supply module
PCI354-1022-38 02088-019 Jasper Electronics Processor module
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