文章基本信息

标题：Automated Valve Fault Detection Based on Acoustic Emission Parameters and Artificial Neural Network
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作者：M. Ali Al-Obaidi Salah ; K.H. Hui ; L.M. Hee 等
期刊名称：MATEC Web of Conferences
电子版ISSN：2261-236X
出版年度：2019
卷号：255
DOI：10.1051/matecconf/201925502013
语种：English
出版社：EDP Sciences
摘要：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.