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S-IDAP Measurements Tab
  • 20 Sep 2024
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S-IDAP Measurements Tab

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Article summary

The Measurements tab is available in View 1 (opening screen). The Measurement tab shows the hierarchy, the readings and the status of the readings.

In a cell, on the right side of the reading, we can have the character L, S, B, W, E, T, G, V or I.

A reading that has been set to Ignored is coloured pink, both in the grid and in the trendplot. If the reading is qualified as a “Warning”, then a yellow cell will be shown and if the reading is qualified as an “Error” then the cell will be shown red.

Long, Short and Base Readings:  L, S and B

When calculating the long and short corrosion rates for each CML/MP, IMS uses only three good thickness readings. Typically, these are: the Latest Reading, the Previous Reading and the First Reading. The thickness readings used in the calculations are indicated as follows:

Reading

Description

Symbol

Latest

Long

L

Previous

Short

S

First

Base

B

Example:

CML ID

Renewal

1-Jun-2008

1-Jun-2002

1-Jun-2000

1-Jun-1998

01A (metric)

5.08mm

7.62mm

8.38mm

8.48mm

8.64mm

01A (U.S. Cust)

0.2 inch

0.3 inch

0.33 inch

0.334 inch

0.34 inch

Designation

Tmin

L  (Long)

S  (Short)

B  (Base)

An example, in the red circle an L, S or B will be displayed.

To learn more about long, short and base readings see Long, Short, and Baseline Readings.

Poor Readings and Bad Readings: W, E and T

IMS will identify some "bad" readings. For example it will always identify when the current thickness reading is greater than the previous reading. These unusual readings will be flagged as follows:

  • Growth Warnings - "W"

  • Growth Errors - "E"

  • Time Errors - "T"

To learn more about these errors see Data Quality (Identifying Anomalies).

Take Note that this "T" has not been implemented in the S-IDAP/DLM algorithm to estimate the Corrosion Rate.

Good, Verified and Ignore:  G, V and I

A reading can be classified as Good, Verified or Ignored. To learn more about reviewing and verifying error reading see Data Quality (Identifying Anomalies).


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