Flexible Acoustic Emission Sensor Signal Classification Using Convolutional Neural Networks for Pipeline Leak Detection
Abstract
This paper presents a method for the real-time detection of pipeline leaks using flexible Acoustic Emission (AE) sensors. The signals gathered from the AE sensor are transformed into RGB images through the application of Mel-spectrogram and color coding. These converted images serve as input for a Convolutional Neural Network (CNN) based on ResNet18. With this approach, both the presence and intensity of leaks in a pipeline can be identified using the AE sensor. The effectiveness of the proposed method was validated through data collected from a testbed featuring a galvanized pipe.
Fields of Study & Subjects
Water Systems and Optimization
Non-Destructive Testing Techniques
Music and Audio Processing
Publication details
- Journal / source
- Journal of the Korean Society for Precision Engineering
- Volume
- 43
- Issue
- 1
- Pages
- 13–19
- Publication date
- 1 January 2026
- Language
- EN