This article proposes a new framework to estimate the degradation level in oil and gas pipelines corroded by internal pitting when operational conditions change over time. Despite the fact that the operational conditions of a pipeline change at various times, this change has not been addressed in the current available pipeline corrosion degradation models. In this framework, a hierarchical Bayesian method and augmented particle filtering are used for data fusion to address this issue. This framework is applied on a case study and the results are compared with the estimations of a state of the art pitting corrosion degradation model.
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