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Fault diagnosis of wind turbine bearing based on stochastic subspace identification and multi-kernel support vector machine

Overview of attention for article published in Journal of Modern Power Systems and Clean Energy, April 2018
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Mentioned by

peer_reviews
1 peer review site

Citations

dimensions_citation
38 Dimensions

Readers on

mendeley
30 Mendeley
Title
Fault diagnosis of wind turbine bearing based on stochastic subspace identification and multi-kernel support vector machine
Published in
Journal of Modern Power Systems and Clean Energy, April 2018
DOI 10.1007/s40565-018-0402-8
Authors

Hongshan Zhao, Yufeng Gao, Huihai Liu, Lang Li

Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 30 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 30 100%

Demographic breakdown

Readers by professional status Count As %
Student > Doctoral Student 6 20%
Student > Bachelor 4 13%
Student > Ph. D. Student 3 10%
Lecturer 2 7%
Lecturer > Senior Lecturer 1 3%
Other 2 7%
Unknown 12 40%
Readers by discipline Count As %
Engineering 10 33%
Computer Science 1 3%
Social Sciences 1 3%
Energy 1 3%
Unknown 17 57%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 1. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 11 April 2018.
All research outputs
#15,504,780
of 23,041,514 outputs
Outputs from Journal of Modern Power Systems and Clean Energy
#75
of 360 outputs
Outputs of similar age
#209,921
of 329,124 outputs
Outputs of similar age from Journal of Modern Power Systems and Clean Energy
#1
of 3 outputs
Altmetric has tracked 23,041,514 research outputs across all sources so far. This one is in the 22nd percentile – i.e., 22% of other outputs scored the same or lower than it.
So far Altmetric has tracked 360 research outputs from this source. They receive a mean Attention Score of 1.5. This one is in the 3rd percentile – i.e., 3% of its peers scored the same or lower than it.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 329,124 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 27th percentile – i.e., 27% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 3 others from the same source and published within six weeks on either side of this one. This one has scored higher than all of them