Research Output
Sensor faults detection and classification using SVM with diverse features
  Sensors in industrial systems fault frequently leading to serious consequences regarding cost and safety. The authors propose support vector machine-based classifier with diverse time- and frequency-domain feature models to detect and classify these faults. Three different kernels, i.e., linear, polynomial, and radial-basis function, are employed separately to examine classifier's performance in each case. Furthermore, the respective kernel scales, δ and p of radial-basis function kernel and polynomial kernel, are varied manually to obtain the optimal values. Leave-one-out cross validation is adopted to overcome the overfitting problem. The dataset was acquired from a temperature-to-voltage converter through Matlab and Arduino Uno microcontroller. The efficiency in terms of percent accuracy of proposed time- and frequency-domain feature models can be seen in experimental results.

  • Date:

    14 December 2017

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  • Funders:

    Historic Funder (pre-Worktribe)


Jan, S. U., & Koo, I. S. (2017). Sensor faults detection and classification using SVM with diverse features. In 2017 International Conference on Information and Communication Technology Convergence (ICTC).



Support Vector Machine, Sensors faults, Fault Classification, Fault Detection, feature extraction

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