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 originating from the many different impact sources and wide ranges of practical operating conditions. In this paper Principal Component Analysis (PCA) is used for selecting vibration feature and detecting different faults in a reciprocating compressor. Vibration datasets were collected from the compressor under baseline condition and five common faults: valve leakage, inter-cooler leakage, suction valve leakage, loose drive belt combined with intercooler leakage and belt loose drive belt combined with suction valve leakage. A model using five PCs has been developed using the baseline data sets and the presence of faults can be detected by comparing the T2 and Q values from the features of fault vibration signals with corresponding thresholds developed from baseline data. However, the Q-statistic procedure produces a better detection as it can separate the five faults completely.
|Journal of Physics: Conference Series
|Published - 28 May 2012
|25th International Congress on Condition Monitoring and Diagnostic Engineering: Sustained Prosperity through Proactive Monitoring, Diagnosis and Management - University of Huddersfield, Huddersfield, United Kingdom
Duration: 18 Jun 2012 → 20 Jun 2012
Conference number: 25
http://compeng.hud.ac.uk/comadem2012/ (Link to Conference Website )