Artificial intelligence-based machine learning considering flow and temperature of the pipeline for leak early detection using acoustic emission

Abstract

The application of the high-frequency Acoustic Emission (AE) system for condition monitoring of the pipeline has been increasing. But the noise of AE signal is essential to reduce the noise and redundant signal due to the high sensitivity transducer. Genetic Algorithm (GA) for feature selection and Principle Component Analysis (PCA) for preprocessing to improve fault classification accuracy are estimated by Artificial Intelligence (AI) based machine learning using Support Vector Machine (SVM). In order to diagnose efficiently leak early detection for pipeline system. In addition, the different critical condition for exciting sources of the tube for heat exchanger of fuel cell are occurred considering crack, temperature and flow of fluid. For preprocessing, envelope analysis is a powerful method for detecting faults of the bearing system, but envelope analysis is not proper for use in the pipeline. Therefore, in this paper, Intensified Envelope Analysis (IEA) for a signal-preprocessing method consisting of envelope analysis and discrete wavelet transform (DWT) is applied. Moreover, a novel mother function optimized for the AE signal. Therefore, preprocessing and feature selection and extraction are estimated for early detection considering the condition of the pipeline through the comparison with them. As the result of classification using SVM one of the machine learning, the performance of GA for feature selection and IEA are more improved than PCA and envelope analysis. This study is focused on the condition of the pipeline to discriminate the condition of crack, temperature and the flow of fluid using AE signal.