Name
Abstract | ||
|---|---|---|
Nowadays, empirical mode decomposition (EMD) and Hilbert spectrum (HS) have been broadly applied on non-stationary and nonlinear signal analysis because it is developed from instantaneous frequency. HS offers a means of signal pattern recognition. But it is the same as other time-frequency distribution methods without giving a quantitative description. Thus, it is not very suitable for real application. In this research, pattern recognition method based on Kolmogorov-Smirnov (K-S) test method is investigated in detail for machine condition monitoring and pattern recognition. For accurate and automatic recognition for the working of machines, K-S test is used to distinguish and recognize the engine condition. Experimental data of a marine diesel engine is used to evaluate the developed methodology for diesel automatic pattern recognition. It can be concluded that this method is very promising for the development of diesel engine preventative maintenance. |
Year | DOI | Venue |
|---|---|---|
2007 | 10.1007/978-3-540-74282-1_62 | ADVANCED INTELLIGENT COMPUTING THEORIES AND APPLICATIONS: WITH ASPECTS OF CONTEMPORARY INTELLIGENT COMPUTING TECHNIQUES |
Keywords | Field | DocType |
K-S test,Hilbert spectrum,preventative maintenance | Reciprocating engine,Hilbert spectrum,Signal processing,Test method,Diesel fuel,Pattern recognition,Computer science,Artificial intelligence,Diesel engine,Instantaneous phase,Hilbert–Huang transform | Conference |
Volume | ISSN | Citations |
2 | 1865-0929 | 0 |
PageRank | References | Authors |
0.34 | 1 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Hongkun Li | 1 | 148 | 12.47 |
| Junlong Zhao | 2 | 0 | 2.37 |
| Xiaojiang Ma | 3 | 4 | 2.33 |