Fan Fault Analysis Based on Time Domain Features and Improved k-means Clustering Algorithm
- https://doi.org/10.2991/iceat-16.2017.28How to use a DOI?
- centrifugal fan, time domain features, improved k-means clustering algorithm
Aiming at the non-stationary and nonlinear characteristics of fan vibration signals, a method based on time domain signal analysis combined with the improved k-mea ns clustering algorithm is proposed. In order to estimate the fault types, peak to peak values of several typical fan fault signals, Hurst exponent and approximate entropy have been extracted and put into the improved k-means clustering classifier as feature vectors. The experiments o n the centrifugal fan- show that: the selected three kinds of time-domain characteristics can reflect the difference between fault s and the effect. Besides, the improved k-means clustering algorithm whose average recognition rate may come up to 88. 67% has a better classification performance compared with the original k-means, and runs much mo re stably.
- © 2017, the Authors. Published by Atlantis Press.
- Open Access
- This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).
Cite this article
TY - CONF AU - C.L. Shao AU - W. Lv PY - 2016/05 DA - 2016/05 TI - Fan Fault Analysis Based on Time Domain Features and Improved k-means Clustering Algorithm BT - Proceedings of the 2016 International Conference on Engineering and Advanced Technology PB - Atlantis Press SP - 130 EP - 135 SN - 2352-5401 UR - https://doi.org/10.2991/iceat-16.2017.28 DO - https://doi.org/10.2991/iceat-16.2017.28 ID - Shao2016/05 ER -