MATEC Web Conf.
Volume 255, 2019Engineering Application of Artificial Intelligence Conference 2018 (EAAIC 2018)
|Number of page(s)||8|
|Section||Smart Manufacturing and Industrial 4.0|
|Published online||16 January 2019|
Automated Valve Fault Detection Based on Acoustic Emission Parameters and Artificial Neural Network
1 Institute of Noise and Vibration, Universiti Teknologi Malaysia, Jalan Sultan Yahya Petra, 54100 Kuala Lumpur, Malaysia
2 Energy and Renewable Energies Technology Center, University of Technology, Baghdad, Iraq
3 School of Engineering, Bahrain Polytechnic, 33349 Isa Town, Kingdom of Bahrain
4 Department of Refrigeration and Air-conditioning, Technical College of Mosul, Northern Technical University, Mosul, Iraq
* Corresponding author: email@example.com
Reciprocating compressor is one of the most popular classes of machines use with wide applications in the industry. However, valve failures in this machine often results unplanned shutdown. Therefore, the effective valve fault detection technique is very necessary to ensure safe operation and to reduce the unplanned shutdown. This paper propose an artificial intelligence (AI) model to detect valve condition in reciprocating compressor based on acoustic emission (AE) parameters measurement and artificial neural network (ANN). A set of experiments were conducted on an industrial reciprocating air compressor with several operational conditions including good valve and faulty valve to acquire AE signal. A fault detection model was then developed from the combination of healthy-faulty data using ANN tool box available in MATLAB. The results of the model validation demonstrated accuracy of valves condition classification exceeding 97%. Eventually, the authors intend to do more efforts for programming this model in smart portable device which can be one of the innovative engineering technologies in the field of machinery condition monitoring in the near future.
© The Authors, published by EDP Sciences, 2019
This is an Open Access article distributed under the terms of the Creative Commons Attribution License 4.0, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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