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An improved sensor fault diagnosis scheme based on TA-LSSVM and ECOC-SVM

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Publication:1621150
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DOI10.1007/s11424-017-6232-3zbMath1401.93193OpenAlexW2766793945MaRDI QIDQ1621150

Fang Deng, Xin Gao, Rui Zhou, Xiaodan Gu

Publication date: 8 November 2018

Published in: Journal of Systems Science and Complexity (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1007/s11424-017-6232-3


zbMATH Keywords

fault detectionfault identificationSVMECOCTA


Mathematics Subject Classification ID

Sensitivity (robustness) (93B35) Learning and adaptive systems in artificial intelligence (68T05) Estimation and detection in stochastic control theory (93E10) Least squares and related methods for stochastic control systems (93E24)




Cites Work

  • Denoising and harmonic detection using nonorthogonal wavelet packets in industrial applications
  • Descriptor reduced-order sliding mode observers design for switched systems with sensor and actuator faults
  • Discriminant analysis based on statistical depth
  • Model-based fault diagnosis techniques. Design schemes, algorithms and tools
  • Parametric and non-parametric combination model to enhance overall performance on default prediction
  • Support-vector networks
  • 10.1162/15324430152733133
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