Abstract detail

107 / 2021-03-30 15:53:58
Visual recognition method of air leaking signal based on convolutional neural network
Visualization of acoustic signals,CNN,Leaking detection,real-time
Special Sessions > Applications of machine learning in vibration and noise problems
Draft Paper Accepted
Li Wang / Wuhan University of Technology
Yongsheng Yu / Wuhan University of Technology
Ziqin Zhou / Wuhan University of Technology
Zhe Wang / Wuhan University of Technology
Peng Song / Wuhan University of Technology
Acoustic signal detection to achieve high accuracy and real-time performance has always been an important issue in the field of acoustics. Convolutional neural network is a new type of artificial neural network method that combines artificial neural network and deep learning technology, and has been widely used in the field of image recognition. In this paper, using the visualization of acoustic signals combined with image recognition methods of convolutional neural networks, an artificial intelligence-based gas leaking signal detection method is proposed.This method converts the acoustic signal into a spectrogram, and uses the convolutional neural network as the input to train, and obtains a gas leaking recognition model with high recognition accuracy. The experimental results show that the model can be accurate, reliable, and real-time online detection of whether there is a gas leaking.

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Important Dates

Abstract Submission Deadline:

 31st March 2021 15th April 2021

Extended Deadline: 1st Aug. 2022

 

Abstract Acceptance:

30th April  2021 Rollover

 

Full Paper Submission Deadline:

30th June 2021  14th July 2021

Extended Deadline: 15th Aug. 2022 

 

Notification of Acceptance:

15th August 2021 1st Sept. 2021

1st Sept. 2022

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