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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489-P1-HI-A20-E GE Relay
489-P5-HI-A20-E GE Enhanced display
IS420ESWAH3A GE Ethernet switch
IS420ESWAH2A GE Ethernet switch
IS420ESWAH1A GE Control system
IS420YDOAS1B GE relay
IS420YAICS1B GE Analog input
IS420UCSDH1A GE processor
IS420UCSCH1C GE One of the UCSC controllers
IS420UCSCH1B GE UCSC controller
IS420UCSBS1A GE Control safety system
IS420UCSBH4A GE High speed application
IS420UCSBH3A GE UCSB controller
IS420UCSBH1A GE controller
IS420UCPAH2A GE controller
IS420UCPAH1A GE processor
IS420PUAAH1A GE Universal input
IS420PSCAH1B GE Communication module
IS420PPNGH1A GE Gateway module equipment
IS420PFFAH1A GE Bus gateway
IS420ESWBH5A GE Single mode fiber interface
IS420ESWBH4A GE ESWB IONet switch
IS420ESWBH1A GE IONet switch
IS420ESWAH5A GE ESWA switches
IS420ESWAH4A GE switch
469-P5-LO-A20-T GE Ethernet connection
469-P5-LO-A20-E GE
469-P5-LO-A20 GE Low control power
469-P5-HI-A20-T-H GE Ethernet port
469-P5-HI-A20-T GE Ethernet communication
469-P5-HI-A20-E-H GE Output relay
469-P5-HI-A20 GE Motor management relay
469-P5-HI-A1-E GE relay
469-P1-HI-A20-E-H GE
469-P1-HI-A1-E-H GE relay
469-P1-HI-A1-E GE Display screen
489-P5-LO-A20 GE Multiplex relay
SR469-P5-HI-A20-H GE relay
SR469-P5-HI-A20-E GE Control power supply
SR469-P5-HI-A20 GE Analog output
469-P5-HI-A20-E GE Control power supply
469-P1-HI-A20-E-H GE Analog output
369-LO-R-M-0-D-0-E GE Output relay
369-L0-R-M-0-D-0-E GE Enhanced motor
369-HI-R-M-F-0-H-E GE Relay
369-HI-R-M-F-0-H-E GE Motor protective relay
369-HI-R-M-0-0-H-0 GE Motor management relay
369-HI-R-M-0-0-0 GE Alternating current motor
369-HI-R-M-0-0 GE Resistance temperature detector
369-HI-R-B-0-E-0-E GE optically isolated
369-HI-R-B-0-0 GE Motor management
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