Predictive Monitoring Toolbox -Release 2...Plot T2 and S2 stats-Plot the S.F.A. monitoring...
Transcript of Predictive Monitoring Toolbox -Release 2...Plot T2 and S2 stats-Plot the S.F.A. monitoring...
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PREDICTIVE MONITORING TOOLBOX-RELEASE 2.0
QUICK DEMO GUIDETADIWA WAUNGANA, 2019
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HOME WINDOW
Load a dataset from an excel sheet or mat file
LoadDataset
Open offline analysis
Offline Analysis
Save the current dataset to a mat file
Export Dataset
Opens groupwise data treatment for the selected of tags
Data Treatment
Open offline SPM
Control Chart
Open window for event definition
Define Events
Open auxiliary tag creation window
Create auxiliary
tags
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LOAD DATA
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APPLY NAME EXCHANGE
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TAG PLOTTING
Sep/0
1/201
3Se
p/08
Sep/1
5
Sep/2
2
Sep/2
9
-20
0
20
40
60
80
100Selected tags from dataset
tag1
tag2
tag3
tag4
tag5
Plots the selected tags from the listed process variables
Plot Tags
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AUXILIARY TAGS
Click to add the highlighted process variable from the home window
Add selected
PV to formula Creates the tag defined by the
tag name and the tag formula
Add auxiliary
tag
Input the auxiliary tag name
Enter auxiliary
Tag NameInput the mathematical formula that defines the auxiliary tag
Enter tag formula
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GROUP MANAGEMENT
Confirms any changes made to the contents of a group
Finish
Double clicking a group from the list of created groups opens the group editing window
Double click a group
Create a group using the highlighted tags from the listed process variables
Create group
Input the group name
Input group name
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DATA TREATMENT
Load limits from a pre-existing mat file
Save the current limits to a mat file
Load/Save Limits
Confirms and saves any processing that was performed on the tags
Complete Data
Processing
Cancels any processing that has been applied in the current processing session
Discard Current Data
Processing
Remove NaNsfrom the data
NaNTreatment
Detect and treat outliers using ‘filloutliers’
Outlier Treatment
Apply a butterworth filter
Filter
Detect and treat samples outside of operational limits
Operational Limits
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DATA TREATMENT CONT’D.
Applies the specified data processing
Applies the specified data processing and plots the raw and treated data
Apply/ Apply
and Plot All treated data will have a suffix to identify it from raw data
The treated data will be added to the list of tags in the home window
Add Tag suffix
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EVENT MANAGEMENT
Defines the event based off the highlighted tag from the home window
Select tag(s) from PV list box
Define an event by manually selecting them from a plot
Define Manually
Plots all existing events on a single graph
Plot all
Visualizes the selected tag(s) so the event may defined manually
Plot (Manual)
Define an event from a formula and comparison
Define using threshold
relationshipVisualize the event before creating it
Plot (Threshold)
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EVENT MANAGEMENT CONT’D.
Adds the created event as a near event
Add as Near event
Adds the created as an event
Add as event
Adds the created event as a process shutdown
Process monitoring is disabled during a shutdown
Add as shutdown
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OFFLINE ANALYSIS
Select an analysis type
Offline Analysis Options
Find which tags correlate highly to a reference tag
Correlation Study
Input the upper and lower thresholds
Positive/Negative Threshold
Selects the highlighted group from the listed groups in the home window
Select Group
Find the correlation between the highlighted tags and the ‘Tag for Correlation’
Run Correlation
Study
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OFFLINE ANALYSIS CONT’D.
Conduct P.C.A. on the group data
Principle Component
Analysis (P.C.A.)
Compare the speed of extracted features
Slow Feature Analysis (S.F.A.)
Scree Plot-Visualize the variance percentage of each PC
Plot Score Visualization-Visualize scores (Correlation for 2/3D or simple plot for 1D)Plot Q and T Stats- Plot the P.C.A. monitoring statistics for this group
P.C.A.
Slowness Assessment-Compare the speed of extracted
Plot slow features-Plot the selected features
Plot T2 and S2 stats- Plot the S.F.A. monitoring statistics for this group
S.F.A.
Calculate and visualize the continuous wavelet transform
Wavelet Transform
(W.T.)
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CONTROL CHART
Proceed to statistical process monitoring-Immediately available for Time Domain
Run SPM
Refreshes the list of available groups for analysis
Refresh
Opens the ‘Group Tags’ window to view the tags contained in the group
Double click a group
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STATISTICAL PROCESS MONITORINGTIME DOMAIN: exampleMat
Choose a window of normal data for the initial model
(1)Train Initial Model
% Explained:Select the minimum percentage of variance to be accounted for by PCs/SFs
Alarm Control Limit Confidence:Select the control limit confidence for alarms
Control Chart
Config.
Select the type of analysis for S.P.M.
Analysis Config.
Runs S.P.M.• Becomes active
after the initial model has been trained
(2) Calculate Monitoring Statistics
Plot the monitoring statistics for the selected analysis method• Enabled after
S.P.M. has been run
(3) Plot Control Chart
Visualize the analysis domain, modelling statistics and model update flags
Plot Modelling
Info.
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The threshold for the modelling statistic, below which it will be considered normal
Enter confidence percentage for analysis statistics; absolute threshold for std difference
Update Threshold
Select a statistic to discern abnormal behaviour
Modelling Statistic
Visualize the modelling parameters
Dynamic Modelling
Input the size of the window to use for dynamic modelling
Update Window
Input how often to attempt model updates• Model updates
are disabled during process shutdowns
Update Frequency
Input the lag of the model window behind the update time
Update Lag
STATISTICAL PROCESS MONITORING CONT’D.TIME DOMAIN: exampleMat
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STATISTICAL PROCESS MONITORING CONT’D.TIME DOMAIN: exampleMat
S.P.M Results: Control Chart
Click to
Zoom
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STATISTICAL PROCESS MONITORING CONT’D.TIME DOMAIN: exampleMat
S.P.M Results: Control Chart
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Generating frequency features for ‘exampleData’:
Frequency Window Length –1440 mins
Step size – 100 samples
Multi-Domain (WT)
Example
CONTROL CHART CONT’D.MULTI-DOMAIN (W.T.): exampleMat
Proceed to statistical process monitoring-becomes active after scale selection is complete
Run SPM
Interactively select scales to use for S.P.M.
Select WT ScalesGenerate new scales from the
available group data using moving window technique
Generate WT Scales
Load existing scales from a mat file
Load WT Scales
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SCALE SELECTIONMULTI-DOMAIN (W.T.): exampleMat
Select the range of scales for each tag
Scale Slider
Average, Max:Select the average or maximum values over each frequency window
End, Middle:Select the value from the end or middle of each frequency window
Scale value
selection
Generates a snapshot image of the interactive graphs for zooming and further analysis
Generate Figure
Confirms the selected scales for SPM
Enables the ‘Run SPM’ in the control chart window
Confirm Scale
Selection
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STATISTICAL PROCESS MONITORINGMULTI-DOMAIN (W.T.): exampleMat
Choose a window of normal data for the initial model
(1)Train Initial Model
Filter type:Select the filter type to filter the generated statisticsFrequency Window:Selected the duration over which the statistics will be filtered
Filter Parameters
Select the type of analysis for S.P.M.
Analysis Config.
Runs S.P.M.• Becomes active
after the initial model has been trained
(2) Calculate Monitoring Statistics
Plot the monitoring statistics for the selected analysis method• Enabled after
S.P.M. has been run
(3) Plot Control Chart
Visualize the analysis domain, modelling statistics and model update flags
Plot Modelling
Info.
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STATISTICAL PROCESS MONITORING CONT’D.MULTI-DOMAIN (W.T.): exampleMat
The threshold for the modelling statistic, below which it will be considered normal
Enter confidence percentage for analysis statistics; absolute threshold for std difference
Update Threshold
Select a statistic to discern abnormal behaviour
Modelling Statistic
Visualize the modelling parameters
Dynamic Modelling
Input the size of the window to use for dynamic modelling
Update Window
Input how often to attempt model updates• Model updates
are disabled during process shutdowns
Update Frequency
Input the lag of the model window behind the update time
Update Lag
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STATISTICAL PROCESS MONITORING CONT’D.MULTI-DOMAIN (W.T.): exampleMat
S.P.M Results: Control Chart
Click to
Zoom
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STATISTICAL PROCESS MONITORING CONT’D.MULTI-DOMAIN (W.T.): exampleMat
S.P.M Results: Control Chart
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