Abstract
The current aging management plans of passive structures in nuclear power plants (NPPs) are based on preventative maintenance strategies. These strategies involve periodic, manual inspection of passive structures using nondestructive examination (NDE) techniques. This manual approach is prone to errors and contributes to high operation and maintenance costs, making it cost prohibitive. To address these concerns, a transition from the current preventive maintenance strategy to a condition-based maintenance strategy is needed. The research presented in this paper develops a condition-based maintenance capability to detect corrosion in secondary piping structures in NPPs. To achieve this, a data-driven methodology is developed and validated for detecting surrogate corrosion processes in piping structures. A scaled-down experimental test bed is developed to evaluate the corrosion process in secondary piping in NPPs. The experimental test bed is instrumented with tri-axial accelerometers. The data collected under different operating conditions is processed using the Hilbert-Huang Transformation. Distributional features of phase information among the accelerometers were used as features in support vector machine (SVM) and least absolute shrinkage and selection operator (LASSO) logistic regression methodologies to detect changes in the pipe condition from its baseline state. SVM classification accuracy averaged 99% for all models. LASSO classification accuracy averaged 99% for all models using the accelerometer data from the X-direction.
Use Of Neural Networks To Identify Transient Operating Conditions In Nuclear Power Plants
