Hit papers significantly outperform the citation benchmark for their cohort. A paper qualifies
if it has ≥500 total citations, achieves ≥1.5× the top-1% citation threshold for papers in the
same subfield and year (this is the minimum needed to enter the top 1%, not the average
within it), or reaches the top citation threshold in at least one of its specific research
topics.
Support vector machine in machine condition monitoring and fault diagnosis
20071.2k citationsAchmad Widodo, Bo‐Suk YangMechanical Systems and Signal Processingprofile →
Peers — A (Enhanced Table)
Peers by citation overlap · career bar shows stage (early→late)
cites ·
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This map shows the geographic impact of Bo‐Suk Yang's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Bo‐Suk Yang with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Bo‐Suk Yang more than expected).
This network shows the impact of papers produced by Bo‐Suk Yang. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Bo‐Suk Yang. The network helps show where Bo‐Suk Yang may publish in the future.
Co-authorship network of co-authors of Bo‐Suk Yang
This figure shows the co-authorship network connecting the top 25 collaborators of Bo‐Suk Yang.
A scholar is included among the top collaborators of Bo‐Suk Yang based on the total number of
citations received by their joint publications. Widths of edges
represent the number of papers authors have co-authored together.
Node borders
signify the number of papers an author published with Bo‐Suk Yang. Bo‐Suk Yang is excluded from
the visualization to improve readability, since they are connected to all nodes in the network.
All Works
20 of 20 papers shown
1.
Niu, Gang, et al.. (2014). 1 Multi-agent decision fusion for motor fault diagnosis.58 indexed citations
2.
Tran, Van Tung & Bo‐Suk Yang. (2010). Machine fault diagnosis and condition prognosis using classification and regression trees and neuro-fuzzy inference systems. Control and Cybernetics. 39(1). 25–54.8 indexed citations
3.
Kim, Jong-Do, Moon-Chul Yoon, Seon–Jin Kim, & Bo‐Suk Yang. (2010). Mode Analysis of Uncoupled System. Journal of the Korean Society of Manufacturing Process Engineers. 9(3). 35–41.3 indexed citations
Gu, Dong-Sik, Eui Hyeok Kim, Joseph Mathew, et al.. (2008). Fault diagnosis of low speed bearing based on relevance vector machine and support vector machine. Faculty of Built Environment and Engineering.8 indexed citations
7.
Yang, Bo‐Suk, et al.. (2008). ACCURATE CONDITION MONITORING USING DATA FUSION AND AUTOMATIC ALARM SETTING TECHNIQUE. 한국소음진동공학회 국제학술발표논문집. 1420–1427.1 indexed citations
8.
Tan, Andy, et al.. (2008). Machine condition prognosis based on regression trees and one-step-ahead prediction. Faculty of Built Environment and Engineering.1 indexed citations
Tan, Andy, et al.. (2007). Machine Prognosis with Full Utilization of Truncated Lifetime Data. QUT ePrints (Queensland University of Technology).7 indexed citations
Tran, Van Tung, et al.. (2006). Fault Diagnosis of Induction Motors using Decision Trees. University of Huddersfield Repository (University of Huddersfield).2 indexed citations
14.
Widodo, Achmad & Bo‐Suk Yang. (2006). Intelligent Fault Diagnosis of Induction Motor Using Support Vector Machines. 364–369.1 indexed citations
15.
Widodo, Achmad, et al.. (2006). Faults Detection and Classification of Induction Motor using Wavelet Support Vector Machine. 79–84.1 indexed citations
Yang, Bo‐Suk, et al.. (1991). An Experimetal Study on the Damping Characteristics of Liquid Sloshing. Journal of the Korean Society for Precision Engineering. 8(1). 96–104.
Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive
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research landscape, it—like all bibliographic datasets—has inherent limitations. These include
incomplete records, variations in author disambiguation, differences in journal indexing, and
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Rankless may not fully capture the entirety of a scholar's output or impact.