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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330500-00-02 Bently Nevada piezoelectric speed sensor
330130-085-02-05 Bently Nevada 3300 XL Extension Cable
330161-02-85-05-94-01-02 Bently Nevada Single triaxial high voltage feedthrough of 3300 system
133292-01 Bently Nevada Low Voltage DC Power Module
3500/15-07-00-00 Bently Nevada 3500/15 Power Module
176449-08 Bently Nevada Rod position monitor
128229-01 Bently Nevada Seismic I/O Module
3500/15-02-02-01 Bently Nevada power module
MS-NAE5510-3 ABB Network Automation Engine Controller
1900-65A-01-01-01-01-01 Bently Nevada Universal Device Monitor
1900/65A-01-01-03-00-00 Bently Nevada Universal Device Monitor
21504-00-28-10-02 Bently Nevada 5mm / 8mm series proximity sensor system
990-05-50-01-00 Bently Nevada Two-Wire Vibration Transmitte
24765-01-01 Bently Nevada Shell Expansion Sensor Assembly
3300/03-01-01 Bently Nevada 3300/03 System Monitor
3300/14-02-20-00 Bently Nevada Dual Thrust Position Monitor
3300/14-02-20-00 Bently Nevada DC power supply
H4136 HIMA H 4136 Relay amplifier in a terminal case
H4116 HIMA H 4116 Relay in terminal block case
H4122 HIMA H 4122 Relay in terminal block case
H7506 HIMA H 7506 Bus terminal
H7506 HIMA H 7506 Bus terminal
H7505 HIMA H 7505 Multifunctional interface converter
F8628X HIMA F 8628X communication module
F8628 HIMA F 8628 Ethernet communication
F8627X HIMA F 8627X ethernet module
F8627 HIMA F 8627 communication module
F8621A HIMA F 8621A Coprocessor module
F7131 HIMA F 7131 Power supply module
F7130A HIMA F 7130A Power supply monitoring with buffer batteries
F7126 HIMA F 7126 Power supply module
F7133 HIMA F 7133 Power distribution module
F6706 HIMA F 6706 2-fold Converter digital/analog
F6705 HIMA F 6705 2-fold Converter digital/analog
F6217 HIMA F 6217 8 fold analog input module
F6221 HIMA F 6221 Analog Input Module
F6220 HIMA F 6220 8 fold thermocouple input module
F6217 HIMA F 6217 8 fold analog input module
F6215 HIMA F 6215 analog input module
F6216 HIMA F 6216 8 fold analog input module
F6213 HIMA F 6213 4x analog input module
F6214 HIMA F 6214 4x analog input module
F3349 HIMA F 3349 digital output module
F3348 HIMA F 3348 digital output module
F3335 HIMA F 3335 digital output module
F3334 HIMA F 3334 4-channel output module
F3333 HIMA F 3333 4 fold output module
F3331 HIMA F 3331 8 fold output module
F3330 HIMA F 3330 8-channel output module
F3322 HIMA F 3322 16 fold output module
F5220 HIMA F 5220 2 fold counter module
F3248 HIMA F 3248 16-fold input module
F3240 HIMA F 32404 16-fold input module
F3237 HIMA F 3237 16-fold input module
F3224 A HIMA F3224A 4 16-fold input module
F3223 HIMA F 3223 4 fold input module
F3222 HIMA F 3222 digital input module
F3221 HIMA F 3221 16-fold input module
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