1. Deep Learning Model for Industrial Leakage Detection Using Acoustic Emission Signal
- Author
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Alireza Alghassi, Mominul Ahsan, Masoumeh Rahimi, and Julfikar Haider
- Subjects
0209 industrial biotechnology ,Computer Networks and Communications ,Computer science ,Feature extraction ,Real-time computing ,Fast Fourier transform ,02 engineering and technology ,Signal ,Fault detection and isolation ,FFT ,020901 industrial engineering & automation ,0202 electrical engineering, electronic engineering, information engineering ,1D convolution neural network ,Leakage (electronics) ,lcsh:T58.5-58.64 ,business.industry ,lcsh:Information technology ,Communication ,Deep learning ,feature extraction ,020208 electrical & electronic engineering ,Wavelet transform ,fault detection ,Human-Computer Interaction ,Anomaly detection ,Artificial intelligence ,business - Abstract
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&rsquo, 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.
- Published
- 2020
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