A fuzzy clustering method for periodic data, applied for processing turbomachinery beamforming maps
| Autor: | Bence Toth, János Vad |
|---|---|
| Rok vydání: | 2018 |
| Předmět: |
Beamforming
Fuzzy clustering Acoustics and Ultrasonics Computer science Mechanical Engineering Feature vector Condensed Matter Physics 01 natural sciences Fuzzy logic 010305 fluids & plasmas Mechanical fan Mechanics of Materials Periodic data 0103 physical sciences Turbomachinery Sound sources 010301 acoustics Algorithm |
| Zdroj: | Journal of Sound and Vibration. 434:298-313 |
| ISSN: | 0022-460X |
| DOI: | 10.1016/j.jsv.2018.08.002 |
| Popis: | In the present paper, the fuzzy c-means method is extended, and an algorithm is proposed for fuzzy clustering of data lying in a feature space of arbitrary dimensions, with one of them being periodic. To aid in determining the optimal number of clusters, the Xie-Beni validity index is extended, to account for the periodicity. Furthermore, the relative weights of the dimensions in the calculation of distances are investigated. The method is incorporated into a procedure for processing turbomachinery beamforming maps. Thus, an objective, robust way of identifying the sound sources being present in such machines is obtained. These properties are ensured by selecting the required parameters through parameter studies. Presented through a case study, the method is used to determine the most significant sound source mechanisms in an axial fan. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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