By Michael Warren

By Lynne Kaley

By Lynne Kaley

Seasoned and knowledgeable inspectors are becoming harder and harder to keep. But using some of the industry documents in a smart way, inspectors with less experience can perform like an inspector with many more years of experience and even develop a great materials specialty with practice.

By Greg Alvarado

API RP 581 provides inspection effectiveness examples for grading historical inspection and planning future inspection to manage risk (see Part 2, Annex C Tables 2.C.3.1 through 2.C.10.4). Tables are provided each damage mechanism and represent inspection strategies that, when executed properly, increase confidence in the true damage state of the equipment in measurable ways. The inspection impacts the calculated damage factor (DF) and probability of failure (PoF) by lowering the amount of uncertainty in the calculation of these two measures. For example, implementing an “A” level inspection strategy for hydrogen induced cracking earns an 80%-100% confidence of finding the damage, if present. A “B” level inspection provides a 60%-80% confidence of finding the damage. These percentages affect the debit that escalates the damage factors and PoF for each mechanism which has a direct impact on the DF and PoF. The effectiveness of an inspection directly affects the calculation of DF and POF by reducing uncertainty of the equipment condition.

Each inspection strategy may consist of more than one inspection technique, provides the amount of inspection coverage required, and identifies the susceptible areas to look for the damage. A unique use of the tables is by factoring in considerations that may not be directly apparent when reading these example tables, for example, NDE operator qualifications. If an NDE operator has not passed a qualification demonstration test for finding cracking in a reactor one could use the tables by lowering the effectiveness of the inspection. If a NDE operator had passed a qualification demonstration test for that mechanism using the proper inspection method with the proper amount of coverage one would achieve an “A” effectiveness credit. However, this operator was not available during the turnaround so a competent, otherwise certified operator did the work. In this case the user may reduce the inspection effectiveness to a “B” level credit, even though the technique for that mechanism and the amount of coverage would have otherwise been an “A”. This is a human factor example.

In a similar manner, the table could be used to take some credit for marginal or qualitative NDE methods used alone or in conjunction with other NDE methods. These methods typically provide little or no quantitative value to the remaining life estimate, such as long range UT and acoustic emission testing. But when added to an equipment strategy that includes methods with more quantitative output such as UT for thickness readings or UT shear wave examinations over a defined area, assigning more inspection credit using these qualitative methods in tandem with others or alone may be justified.

These are just two examples demonstrating value-added use of the inspection effectiveness tables. It is important to be consistent, self-critical and achieve the confidence percentages for each level of effectiveness as defined in API RP 581, 3^rd Edition, as shown in Part 2, Annex C.

Please let us know how you are using the tables. Do you adjust or modify them to represent your company’s perspective on effectiveness or do you use them as they are (keep in mind they are only examples)? Do you modify them to include NDE technologies that otherwise prove difficult to assign a quantitative benefit to? Do you modify them to account for human factors? Or other?  Thank you for your input in advance.

By Greg Alvarado

Many RBI software platforms are available. They are based on various methodologies, some public, some private, some qualitative, some quantitative and anywhere in between. Some owner/operators use RBI to better manage risks while some do RBI to save money. The objectives of this entry are to:

1. Clarify the data requirement differences between GE/Meridium’s 580 qualitative RBI module and the 581 quantitative RBI module
2. Point out some outcomes of using either
3. Get some feedback from users on their experiences

There is a common misunderstanding of when to use the damage models in the 581 module to calculate corrosion rates and damage susceptibilities. The damage models from API RP 581, which are in the software, are meant to err to the conservative. These are not required models or entry fields. Since most owner operators have performed damage mechanisms reviews which provide this information it is not necessary to use the damage models. So, only approximately 10-20% additional data is needed to perform quantitative analysis in this comparison.

Based on recent projects, comparisons of quantitative modeling vs. qualitative have shown that quantitative models:

• Are more dynamic (over time, today and tomorrow) versus static (today) risk modeling and predictions.
• Are more fact based and guide the user more systematically through the process producing greater reproducibility and consistency. Qualitative methods are prone to being heavily opinion based.
• Enable “what if” modeling, predict future conditions, and produce discreet PoF, CoF and risk values, have greater bandwidth for metrics and KPIs for managing risk, risk reduction vs. cost, risk reduction over time, etc.

We would love to hear from you, how does this compare with your experiences? What types of metrics are you using? If you are using qualitative methods, what benefits are you seeing? Thanks in advance for your input.