Abstract
Reciprocating compressors play a major part in many industrial systems and faults occurring in them can degrade performance, consume additional energy, cause severe damage to the machine and possibly even system shut-down. Traditional vibration monitoring techniques have found it difficult to determine a set of effective diagnostic features due to the high complexity of the vibration signals because of the many different impact sources and wide range of practical operating conditions. This paper focuses on the development of an advanced signal classifier for a reciprocating compressor using vibration signals. Artificial Neural Networks (ANN) and Support Vector Machines (SVM) have been applied, trained and tested for feature extraction and fault classification. The accuracy of both techniques is compared to determine the optimum fault classifier. The results show that the model behaves well, and classification rate accuracy is up to 100% for both binary classes (a single fault present in the compressor) and multi-classes (three faults present).
Original language | English |
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Title of host publication | 17th International Conference on Automation and Computing, ICAC 2011 |
Publisher | IEEE |
Pages | 213-218 |
Number of pages | 6 |
ISBN (Electronic) | 9781862180987 |
ISBN (Print) | 9781467300001 |
Publication status | Published - 21 Nov 2011 |
Event | 17th International Conference on Automation and Computing - Huddersfield, United Kingdom Duration: 10 Sep 2011 → 10 Sep 2011 Conference number: 17 http://www.wikicfp.com/cfp/servlet/event.showcfp?eventid=14139©ownerid=20561 (Link to Event Details) https://www.tib.eu/en/search/id/TIBKAT%3A73636952X/ |
Conference
Conference | 17th International Conference on Automation and Computing |
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Abbreviated title | ICAC 2011 |
Country/Territory | United Kingdom |
City | Huddersfield |
Period | 10/09/11 → 10/09/11 |
Internet address |