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| Content Provider | The American Society of Mechanical Engineers (ASME) Digital Collection |
|---|---|
| Author | Cheng, Wei Lee, Seungchul Zhang, Zhousuo He, Zhengjia |
| Copyright Year | 2012 |
| Abstract | Most of blind source separation problems are carried out with a priori knowledge of the source numbers. However, for source separation-based machinery condition monitoring and fault diagnosis, it is a challenge work to determine the number of sources for a well source separation due to complex structures and nonlinear mixing mode. Therefore, source number estimation is a necessary and important procedure prior to source separation and further diagnosis work. In this paper, we focus on a novel source number estimation method based on independent component analysis (ICA) and clustering evaluation analysis, and investigate the performances of different dissimilarity measures of ICA-based source number estimations with typical mechanical vibration signals. Our work contributes to find an effective solution of source number estimation for source separation-based machinery condition monitoring and fault diagnosis. |
| Sponsorship | Manufacturing Engineering Division |
| Starting Page | 683 |
| Ending Page | 688 |
| Page Count | 6 |
| File Format | |
| ISBN | 9780791854990 |
| DOI | 10.1115/MSEC2012-7340 |
| Volume Number | ASME 2012 International Manufacturing Science and Engineering Conference |
| Conference Proceedings | ASME 2012 International Manufacturing Science and Engineering Conference collocated with the 40th North American Manufacturing Research Conference and in participation with the International Conference on Tribology Materials and Processing |
| Language | English |
| Publisher Date | 2012-06-04 |
| Publisher Place | Notre Dame, Indiana, USA |
| Access Restriction | Subscribed |
| Subject Keyword | Condition monitoring Fault diagnosis Vibration Independent component analysis Blind source separation Signals Separation (technology) Machinery |
| Content Type | Text |
| Resource Type | Article |
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