Sensor faults detection and classification using SVM with diverse features

Jan, Sana Ullah; Koo, In Soo

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.

Citation

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

• 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