Application of nonlinear feature extraction and support vector machines for fault diagnosis of induction motors
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Three most popular approaches for dimension reduction in medical domain are PCA, ICA, and LDA, which statistically select the subset of most significant features.
“Three most popular approaches for dimension reduction in medical domain are PCA, ICA, and LDA [66] which are statistically selecting the subset of the most significant features [45,67].”
From Data Mining for Wearable Sensors in Health Monitoring Systems: A Review of Recent Trends and Challenges (2013) · cites this paper as [66]
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- Authors
- Widodo, A., & Yang, B.-S.
- Affiliations
- Pukyong National University (KR)
- Corresponding author
- Yang, Bo-Suk
- ORCID
- Widodo, Achmad
- Venue
- Expert Systems with Applications
- Publisher
- Elsevier BV
- Published
- 2007-07
- Volume
- 33
- Issue
- 1
- Pages
- 241-250
- ISSN
- 0957-4174 (ISSN-L)
- OpenAlex
- W2051463173
- Cited by
- 292
- References
- 0
- Citations by year
- 2026: 5 · 2025: 10 · 2024: 7 · 2023: 11 · 2022: 15 · 2021: 8 · 2020: 19 · 2019: 16 · 2018: 13 · 2017: 12 · 2016: 14 · 2015: 14 · 2014: 14 · 2013: 28 · 2012: 24
- FWCI
- 24.39
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Expert Systems with Applications (publishedVersion)
- Topics
- Physical Sciences › Engineering › Control and Systems Engineering › Machine Fault Diagnosis Techniques
- Physical Sciences › Engineering › Control and Systems Engineering › Fault Detection and Control Systems
- Physical Sciences › Engineering › Electrical and Electronic Engineering › Sensorless Control of Electric Motors
- Keywords
- induction motor fault diagnosis, nonlinear feature extraction, support vector machine, principal component analysis, independent component analysis, kernel trick, multi-class classification, kernel selection
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