Pipeline Fault Diagnosis Using Wavelet Entropy and Ensemble Deep Neural Technique
The maintenance of pipelines is essential for the safe and cost effective transport of important fluids such as water, oil, and gas. The early detection of pipeline faults is vital for avoiding material and economic losses, and more importantly for ensuring the safety of both human life and the environment. This paper proposes a methodology for early fault detection in pipelines using an acoustic emission (AE) based technique. The proposed method incorporates wavelet entropy analysis of the AE signals and ensemble deep neural networks for the effective detection of different types of faults in a pipeline. The proposed method is tested on an experimental testbed, and the results indicate that it can detect various faults in the pipeline with an average accuracy of 96%.
KeywordsPipeline fault diagnosis Acoustic emission Wavelet entropy Ensemble deep neural network
This work was supported by the Korea Institute of Energy Technology Evaluation and Planning (KETEP) and the Ministry of Trade, Industry & Energy (MOTIE) of the Republic of Korea (Nos. 20162220100050, 20161120100350, 20172510102130). It was also funded in part by The Leading Human Resource Training Program of Regional Neo industry through the National Research Foundation of Korea (NRF) funded by the Ministry of Science, ICT and future Planning (NRF-2016H1D5A1910564), and in part by the Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education (2016R1D1A3B03931927).
The authors declare that there is no conflict of interest regarding the publication of this manuscript.
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