Reviewing IOW Exceedances and Analyzing data

The IOW Exceedances are calculated based on the limits and exceedance times that you have set for each IOW (see IOW Record). In turn the IOW Exceedances then sets the Process Data Indicator status.

IOW Data can be reviewed and analyzed from both the IOW Details Page and the CL Details Page.

IOW Exceedances 

Exceedances can be found on both the CL Details Page and IOW Details Page.

Exceedance Tracking Requirements

For an IOW Exceedance to be tracked at least the following must be defined for the IOW:

• 1 target value (Target Min or Target Max);
• 1 integrity value (Integrity Min or Integrity Max); and
• 1 of the 3 fields that are necessary to calculate the exceedance:
  • The Maximum time for a single exceedance
  • The Maximum time for the cumulative exceedance, or
  • The Maximum number of single exceedances allowed.

(You can off course always define more limits than the required minimum.)

Viewing from the CL Details Page

To view the IOW Exceedances from the CL Details Page:

• Scroll to the Integrity Operating Windows section.
• Click the Exceedance (IOW) tab.
• Select the desired timespan.

If any exceedances were found in the selected timespan, it will be shown.

To view the IOW Exceedances, scroll to the Integrity Operating Windows section and click the Exceedances (IOW) tab.

Viewing from the IOW Details Page

To view the IOW exceedances from the IOW Details Page:

• Scroll down to the Exceedance (IOW) section.
• Select the desired timespan.

If any exceedances were found in the selected timespan, it will be shown.

Setting the Status

You can set the Status of an exceedance. To do so:

• Select the Exceedance graph card.
• Click on the Status.
• Select the desired Status from the dropdown.  

Changing a Status. 

Adding Notes

You can add a note of an exceedance:

• Select the Exceedance graph card.
• Click in the Note area.
• Add your note.

This Note will show as a Comment in the PI Exceedance section on the IOW Details Page.  

Expanding Graphs

Take note that you can expand a graph for better view. 

To expand a graph:

• Hover with your mouse over the right top corner of the graph card.
• Click on the expand button that will appear.

Hover over the corner to see the expand button.

 When expanded, the percentage of exceedance will also show on the graph.

An expanded graph. 

Analyzing IOW Variables

To further analyze the IOW variables for the selected period:

• Select the Analyze tab.

To analyze the data, select the Analyze Tab. 

Viewing and Comparing Tags

Use the two top windows and bottom right window to plot tags over time (use the Time(M) tab if you want to plot multiple tags on top of each other), or make Histograms, Scatter plots, Correlation charts, and Box plots. For more information on the different types of plots see IOW Analysis Tab.

Creating Virtual Tags for Analysis

To create Virtual Tags to use in an analysis:

• Go to the bottom left window and select the Virtual Tags tab.

Select the Virtual Tags tab in the bottom left window.

 

• Specify a Name.
• Create an Expression (the green buttons will require you to select a tag).
• Click Add to save your virtual tag.

Note: This virtual tag can now be plotted with your other tags.

Cleaning Data

To clean your data:

• Go to the left bottom window and select the Data Cleaning tab.
• In the Detection section: select, for each variable, what anomalies to detect:
  • Out of range: Out of range data are identified based on the min and max limits. Note: Use the Min/Max button to (re)set the range limits to the overall min. and max. of the data.
  • Flat segment: Flat segments in the data are identification based on two parameters – “minimum tolerance” (the variance below which a data segment is classified as flat) and “maximum # repeats” (the length of flat segment allowed, before classified as flat).
  • Constant slope: Constant slopes in the data are identification in the same way as the flat segment. It is based on two parameters – “minimum tolerance” and “maximum # repeats”.
  • Global outlier: Global outliers are identification based on three parameters – “sigma factor” (the sigma (or standard deviation) factor determines the tolerance allowed before classified as an outlier - the greater the sigma factor, the more tolerance and thus less outliers), “outlier points” (i.e. number of consecutive outliers allowed before classified as an outlier) and “trimmed percentage” (a percentage of data points are removed from each tail of the distribution and the trimmed mean is then calculated from the remaining (e.g. 90%) data points).
  • Local outlier: Local outliers are identified similarly to global outliers, but with one additional parameter – “moving window” (this is the length of the moving time window – it is to compensate for local data fluctuation).

Note: You can click the Defaults button to recover the default settings.

• Click Detect (1) to detect data based on set Detection criteria. Note: Detection can be applied to Raw or Detected data. If Raw data is selected, all previous detection results are reset.
• In the Cleaning section, for each variable, review the cleaning rules - linear interpolation, or mean filling, or leave as missing and ranges (k1 and k2). Note: You can click the cleaning’s Defaults button to recover the default settings.
• Click Clean (2) to clean the detected data based on the rules set in the Cleaning section.
• To view the Detected Data and Clean Data, click on these tabs in the top right window. For an explanation of the identified anomalies, click the Anomaly code button.

The Detected and Cleaned data are shown in the top right window.