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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TRICONEX AO3481 Analog Output module TRICON AO3481
TRICONEX EPI3382 simulates the input module TRICON EPI3382
TRICONEX MP6004 Analog output module TRICON MP6004
TRICONEX PI3351 Simulates the input module TRICON PI3351
TRICONEX PM6301A Analog Output module TRICON PM6301A
TRICONEX PI3381 simulates the input module TRICON PI3381
TRICONEX 3708EN Analog input module 3704E TRICON 3708EN
TRICONEX 3706A Thermocouple analog input module TRICON 3706A
5SHY4045L0003 3BHB021400 3BHE019719R0101 GVC736BE101 Diode and grid drive circuits
3500/54-03-00 Electronic Speed Detection System 3500/54
DSTS106 3BSE007287R1 Trigger Pulse regulator Advant main process control system
DSTS105 3BSE007286R1 is used to simulate the standby regulator DSTS105 ramp unit
DI650 3BHT300025R1 Digital input 32 channel DI650
3500/92-02-01-00 Communication Gateway 3500/92-04-01-00
TC515V2 3BSE013284R1 AF100 Twisted-pair repeater Advant Field Bus 100 interface
TC512V1 3BSE018059R1 RS485 Twisted Pair modem Advant Controller
DSAO130A 3BSE018294R1 Analog output board 16-channel S100 I/O module
DSTA155P 3BSE018323R1 Connects Unit 14 thermocouple S100 I/O terminal unit
ABB SYN5202-0277 Synchronous device
DSTA002B 3BSE018317R1 Analog input board connected to the unit S100 I/O module
DSTA135 3BSE018315R1 input board analog long connection unit S100 I/O module
DSTA001B 3BSE018316R1 Analog long connection unit S100 I/O module
DSTD190V1 3BSE018314R1 connects to unit 32 Ch control system Advant
DSTA181 3BSE018312R1 Connection unit S100 I/O module for simulation
DSTA156B 3BSE018310R1 connects unit 32 Ch control system Advant
DSDI120AV1 3BSE018296R1 Digital input pad 32 S100 I/O module
CI522AK04 3BSE018451R1 Communication module AF100 interface kit
CI522AK02 3BSE018449R1 Communication bus module AF100 interface
CI522AK01 3BSE018448R1 Field bus interface module AF100 interface suite
CI522AK01 3BSE018448R1 Field bus interface module AF100 interface suite
DSTD108LP 3BSE018335R1 Connection unit S100 I/O terminal unit with 8 relays
DSTD108P 3BSE018333R1 Connects to the Unit 8 Ch S100 I/O terminal unit
DSTD196P 3BSE018332R1 Connects to the Unit 8 Ch S100 I/O terminal unit
DSTD109P 3BSE018327R1 Connects to Unit 8 Ch S100 I/O terminal unit
DSAI130K18 3BSE019910R1 Analog input 16-channel S100 I/O module
DSBC176 3BSE019216R1 bus expansion board S100 I/O module
DSBC176K01 3BSE019956R1 S100 I/O Connection Kit Bus Coupler Kit
TC561V2 3BSE022179R1 optical modem S100 I/O bus communication interface
DSDI110AK14 3BSE019927R1 Digital Input Kit 32 channel S100 I/O module
DSSX166 5347049-CR Power distribution terminal AC 400 power supply
CI535V24 3BSE022158R1 Main interface module AC 400 Communication module
DSSR170 48990001-PC DC voltage regulator – Input VDC output Input S100 I/O power module
CI545V01 3BUP001191R1 Precision Ethernet submodule AC400 communication module
CI532V09 3BUP001190R1 Submodule Interface AC 400 communication module
DSTX170 57160001-ADK Connection unit S100 I/O terminal unit
ABB DSTA145 57120001-HP Analog Board Connection Device S100 I/O terminal Unit
ABB DSBB188 3BSE005004R1 Power bus Advant controller
ABB DSSS171 3BSE005003R1 Switch Voting unit Advant controller
DSBC174 3BSE012211R1 bus expander S100 I/O bus
133396-01 Overspeed detection I/O module Bently Nevada
F6217 984621702 8-channel analog input module
HIMA F3237 984323702 Channel 16 input module
F3330 984333002 Channel 8 output module
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