During our investigations and analysis, we recorded several suggestions to mitigate the possible risk of unreliable inspection data. To accomplish this, it is necessary to take action from the root cause, which equals to the cause of data anomalies.
Table 33 ‐ List of Suggested Approach to Mitigate Risk Target Recommendation Suggested
Approach Description
Governing
Assessing additional need of procedure
Improveme nt and
a) NDT procedure to be revised to add more specific instructions (for example to insert coordinate, to not allowing null value and to take last inspection value as alternative or else declare that inspection is cancelled).
b) NDT procedure to be updated to include new standards, especially ISO 13588:2012, Non‐destructive testing of welds ‐ Ultrasonic testing ‐ Use of automated phased array technology.
c) The technical library was also found to be outdated and procedures were not updated accordingly, thus it is advantageous to schedule tasks of reviewing procedure and related standards in timely manner.
Meanwhile, governing documents that need to be in place are:
a) Inspection planning procedure to be created to provide guidelines for Inspection Planners. Which the procedure would also instructs to include inspection coordinate in the planning,
b) Procedure to guide NDT Inspectors in how to use NDT software,
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Table 33 ‐ List of Suggested Approach to Mitigate Risk Target Recommendation Suggested
Approach Description
c) Procedure that outline standard for NDT reporting,
d) Reminder‐posters those are easy to be noticed. Such as posters of NDT techniques or equipment maintenance.
Refresher course for NDT Inspectors
NDT posters
Procedures in place
Continuous developme nt campaign
Rasmussen (1983) suggested skill‐ruled‐knowledge (SRK) framework that categorized human cognitive control mechanism corresponds to decreasing levels of familiarity with the environment or task; they are:
skill‐based behaviour (error without conscious control),
rule‐based behaviour (error structured by feed‐forward cognitive control ), and
Knowledge‐based behaviour (error is goal‐controlled and knowledge‐based).
As later known, all of the NDT Inspectors working on entire three platforms are
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Table 33 ‐ List of Suggested Approach to Mitigate Risk Target Recommendation Suggested
Approach Description
to be regularly reminded to improve work quality as a continuous development. In
1. Abnormal deviations in measurement data 2. Changes in wall thickness
3. Changes in corrosion rate 4. Cancelled inspections Revamp false
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Table 33 ‐ List of Suggested Approach to Mitigate Risk Target Recommendation Suggested
Approach Description
example and implemented when applicable :
Figure 53 ‐ Revamping Engineering Database
Applying Sizing Tolerance
Apply sizing tolerance technique on measurement result
To anticipate the unreliability of data, sizing tolerances shall be implemented to acquire a possible variation of results, thus producing a range of possible results. DNV (DNV, 2010b) has similarly outlined the idea to be implemented as statistical model.
Assuming NDT measurement data is normal distributed, the approach determine confidence level to establish standard deviation (σ) of measurement value. Alternative approach is to increase the number of standard deviation to increase the confidence level (Westwood and Hopkins, 2004), i.e. with three sigma rule as shown on figure X.
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Table 33 ‐ List of Suggested Approach to Mitigate Risk Target Recommendation Suggested
Approach Description
Figure 54 ‐ Three Sigma Rule of Normal Distribution (Westwood and Hopkins, 2004)
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Table 33 ‐ List of Suggested Approach to Mitigate Risk Target Recommendation Suggested
Approach Description
Westwood and Hopkins (2004) relates standard deviation (σ2) of measurement with the confidence level and give estimation of possible measurement range. For example, if denotes the th measurement taken on an inspection point with standard
deviation of σ2, then the true measurement is in the range between (Westwood and Hopkins, 2004):
3 3 (Eq. 5)
From here we could utilize the percentage of error from previous section (5.2). Since the reliability of data‐set ( ) would give estimation that a certain number of
measurement are acceptable, it shall express confidence level of the data set (see
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Table 33 ‐ List of Suggested Approach to Mitigate Risk Target Recommendation Suggested
Approach Description
measurements within period of time. If denotes the th measurement taken on an inspection point, and denotes the time when inspection was taken. Then the
degradation rate ( ) can be determined from two different inspection as shown on Eq.
6 (Pandey and Lu, 2013).
(Eq. 6)
However, Equation 6 can only be applied on ideal state where there is no anomaly present, i.e. 1 (see 5.2). In condition where anomaly is present, the anomaly at the th measurement (Ei) shall be subtracted to to generate the actual measurement ( ):
(Eq. 7)
Thus the actual degradation rate ( ) would deviate from Equation 6 (see Eq. 8).
(Eq. 8)
From previous section (5.2.), we gather information of the error percentage from various data‐set. The percentage of error expresses that a certain number of
measurement contain anomaly and would estimate the unreliability ( ) of the data‐
set (see Eq. 4). We have also discussed that the reliability and unreliability of the data‐
set are equivalent to the probability of the state of data [ ] (Aven, 1992):
1 Reliability of data‐set, and 0 Unreliability of data set
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Table 33 ‐ List of Suggested Approach to Mitigate Risk Target Recommendation Suggested
Approach Description
Thus, the unreliability of data‐set ( ) would estimates the probability of error of the respected data‐set, i.e. . The estimation of sizing error (E) for the respected data‐set would be equivalent to the unreliability of the data‐set, i.e.
. Then the error sizing for specific measurement can be written as in Equation 9.
(Eq. 9)
According to Westwood and Hopkins (2004) the distribution of measurements error during inspection is normally distributed. Then we can estimate the range of actual measurement ( ) as in Equation 10.
(Eq. 10)
Further, since the value of g is constant for specific data‐set, this indicates that , which gives us the estimation of error on the actual degradation rate ( ) as in Equation 11.
(Eq. 11)
Then the variance range for degradation rate can be modelled as on Equation 12.
(Eq. 12)
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