The Importance of  Feedback from Failure&root Causes Analysis in RBI Studies

The API Risk-Based Inspection (API RBI) methodology is used to manage the overall risk of a plant by focusing inspection efforts on the process equipment with the highest risk. API RBI provides the basis for making informed decisions on inspection frequency, the extent of inspection, and the most suitable type of NDE. In most processing plants, a large percent of the total unit risk will be concentrated in a relatively small percent of the equipment items. These potential high-risk components may require greater attention, perhaps through a revised inspection plan. The cost of the increased inspection effort may sometimes be offset by reducing excessive inspection efforts in the areas identified as having lower risk.

Where? When? How? The Output of the RBI analysis is a optimized inspection plan that gives answers to these “inspection” questions. It is obvious that in this context the input data collection represents an important/strategic part of the complex analysis in order to get better and reliable results from the model. However, the feedback from the plant is sometimes different because, in spite of the application of optimized and well “brain stormed” inspection plans on the basis of the RBI model, failures can affect components in a unplanned location and with non foreseen mode/propagation rate. It is normal in a real world, that is governed by natural laws, to find obstacles in foreseeing with great reliability by models & data exactly what is going on out there. The feedback from structured failure analysis and root&latent cause analysis can be a very important added value to RBI analysis in order to know what are the real threats for the reliability of the plant analyzed.