PREDICTIVE MONITORING TOOLBOX-RELEASE 2.0

QUICK DEMO GUIDETADIWA WAUNGANA, 2019

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

LOAD DATA

APPLY NAME EXCHANGE

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

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

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

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

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

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)

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

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

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.)

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

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.

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

STATISTICAL PROCESS MONITORING CONT’D.TIME DOMAIN: exampleMat

S.P.M Results: Control Chart

Click to

Zoom

STATISTICAL PROCESS MONITORING CONT’D.TIME DOMAIN: exampleMat

S.P.M Results: Control Chart

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

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

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.

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

STATISTICAL PROCESS MONITORING CONT’D.MULTI-DOMAIN (W.T.): exampleMat

S.P.M Results: Control Chart

Click to

Zoom

STATISTICAL PROCESS MONITORING CONT’D.MULTI-DOMAIN (W.T.): exampleMat

S.P.M Results: Control Chart

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