An approach for identifying corrosion damage from acoustic emission signals using ensemble empirical mode decomposition and linear discriminant analysis

Abstract

Mechanical structures, such as pressure vessels and pipes, need careful inspection and monitoring to avoid serious corrosion failure. Detecting and identifying corrosion damage from acoustic emission (AE) signals is of significant importance for the safety and reliability of engineering structures in structural health monitoring. The identification accuracy largely depends on how well the damage features are being used. This paper presents a new approach for extracting effective damage features and accurately identifying different damage from AE signals during corrosion monitoring. Specifically, the proposed approach combines ensemble empirical mode decomposition and linear discriminant analysis to analyze the AE signals generated from an intergranular corrosion process. The results show that three damage modes, including environmental noise, intergranular corrosion, and the formation and propagation of cracks can be successfully detected and identified from complicated AE waveforms. The proposed approach is capable of providing reliable, direct and visualized corrosion damage detection and identification in structural health monitoring. Results from this study will guide complementary efforts aimed at detecting and identifying different damage from AE signals, and providing supporting knowledge regarding the industrial application of AE monitoring.