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Deep Learning Model for Industrial Leakage Detection Using Acoustic Emission Signal

1
School of Electrical and Computer Engineering, Shiraz University, Shiraz 71557-13876, Iran
2
The Advanced Remanufacturing and Technology Centre (ARTC)-A*Star, 3 CleanTech Loop, #01/01, CleanTech Two, Singapore 637143, Singapore
3
Department of Engineering, Manchester Metropolitan University, John Dalton Building, Chester St, Manchester M1 5GD, UK
*
Author to whom correspondence should be addressed.
Received: 23 September 2020 / Revised: 27 October 2020 / Accepted: 30 October 2020 / Published: 1 November 2020
Intelligent fault diagnosis methods have replaced time consuming and unreliable human analysis, increasing anomaly detection efficiency. Deep learning models are clear cut techniques for this purpose. This paper’s fundamental purpose is to automatically detect leakage in tanks during production with more reliability than a manual inspection, a common practice in industries. This research proposes an inspection system to predict tank leakage using hydrophone sensor data and deep learning algorithms after production. In this paper, leak detection was investigated using an experimental setup consisting of a plastic tank immersed underwater. Three different techniques for this purpose were implemented and compared with each other, including fast Fourier transform (FFT), wavelet transforms, and time-domain features, all of which are followed with 1D convolution neural network (1D-CNN). Applying FFT and converting the signal to a 1D image followed by 1D-CNN showed better results than other methods. Experimental results demonstrate the effectiveness and the superiority of the proposed methodology for detecting real-time leakage inaccuracy. View Full-Text
Keywords: 1D convolution neural network; FFT; fault detection; feature extraction 1D convolution neural network; FFT; fault detection; feature extraction
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MDPI and ACS Style

Rahimi, M.; Alghassi, A.; Ahsan, M.; Haider, J. Deep Learning Model for Industrial Leakage Detection Using Acoustic Emission Signal. Informatics 2020, 7, 49. https://0-doi-org.brum.beds.ac.uk/10.3390/informatics7040049

AMA Style

Rahimi M, Alghassi A, Ahsan M, Haider J. Deep Learning Model for Industrial Leakage Detection Using Acoustic Emission Signal. Informatics. 2020; 7(4):49. https://0-doi-org.brum.beds.ac.uk/10.3390/informatics7040049

Chicago/Turabian Style

Rahimi, Masoumeh; Alghassi, Alireza; Ahsan, Mominul; Haider, Julfikar. 2020. "Deep Learning Model for Industrial Leakage Detection Using Acoustic Emission Signal" Informatics 7, no. 4: 49. https://0-doi-org.brum.beds.ac.uk/10.3390/informatics7040049

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