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Sensor faults detection and classification using SVM with diverse features

Jan, Sana Ullah; Koo, In Soo

Authors

In Soo Koo



Abstract

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.

Presentation Conference Type Conference Paper (Published)
Conference Name 2017 International Conference on Information and Communication Technology Convergence (ICTC)
Start Date Oct 18, 2017
End Date Oct 20, 2017
Online Publication Date Dec 14, 2017
Publication Date 2017
Deposit Date Oct 21, 2022
Publisher Institute of Electrical and Electronics Engineers
Book Title 2017 International Conference on Information and Communication Technology Convergence (ICTC)
DOI https://doi.org/10.1109/ictc.2017.8191044
Keywords Support Vector Machine, Sensors faults, Fault Classification, Fault Detection, feature extraction
Public URL http://researchrepository.napier.ac.uk/Output/2937000