Name
Title | ||
|---|---|---|
Wavelet packet analysis and empirical mode decomposition for the fault diagnosis of reciprocating compressors. |
Abstract | ||
|---|---|---|
This paper investigates the use of advance signal processing techniques for the classification of four common reciprocating compressor conditions. Non-stationary vibration signals from the compressor are decomposed based on WPT (Wavelet Packet Transform) and EMD (Empirical Mode Decomposition) and harmonic changes of the optimal components of each technique is used for condition monitoring and diagnostics of the machine. Performance comparison of WPT and EMD is done for the envelope analysis of the optimal wavelet packet and the IMF (Intrinsic Mode Function) component that can extract salient features of the four compressor conditions including one normal and three fault conditions (intercooler leakage, discharge valve leakage and the combination of intercooler and discharge valve leakage). Harmonic changes produced after envelope analysis of the best packet and IMF component are used for classification and results show that WPT technique is more robust and effective in fault classification compared to EMD. |
Year | Venue | Field |
|---|---|---|
2017 | ICAC | Signal processing,Control theory,Harmonic,Gas compressor,Condition monitoring,Reciprocating compressor,Engineering,Wavelet packet decomposition,Wavelet,Hilbert–Huang transform |
DocType | Citations | PageRank |
Conference | 0 | 0.34 |
References | Authors | |
3 | 4 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Ugonnaya E. Muo | 1 | 0 | 0.34 |
| Misan Madamedon | 2 | 0 | 0.34 |
| Andrew Ball | 3 | 72 | 17.29 |
| Fengshou Gu | 4 | 23 | 23.43 |