Pipe thinning model development for direct current potential drop data with machine learning approach

Abstract

The accelerated corrosion by Flow Accelerated Corrosion (FAC) has caused unexpected rupture of piping, hindering the safety of nuclear power plants (NPPs) and sometimes causing personal injury. For the safety, it may be necessary to select some pipes in terms of condition monitoring and to measure the change in thickness of pipes in real time. Direct current potential drop (DCPD) method has advantages in on-line monitoring of pipe wall thinning. However, it has a disadvantage in that it is difficult to quantify thinning due to various thinning shapes and thus there is a limitation in application. The machine learning approach has advantages in that it can be easily applied because the machine can learn the signals of various thinning shapes and can identify the thinning using these. In this paper, finite element analysis (FEA) was performed by applying direct current to a carbon steel pipe and measuring the potential drop. The fundamental machine learning was carried out and the piping thinning model was developed. In this process, the features of DCPD to thinning were proposed. Keywords: Flow accelerated corrosion, Piping thinning, Direct current potential drop, Machine learning