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Sensors 2016, 16(12), 2069; doi:10.3390/s16122069

Fault Detection Using the Clustering-kNN Rule for Gas Sensor Arrays

1
School of Electrical Engineering and Automation, Harbin Institute of Technology, Harbin 150080, China
2
Department of Electrical and Computer Engineering, McGill University, Montreal, QC H3A 0E9, Canada
*
Author to whom correspondence should be addressed.
Academic Editor: Vittorio M. N. Passaro
Received: 21 October 2016 / Revised: 28 November 2016 / Accepted: 30 November 2016 / Published: 6 December 2016
(This article belongs to the Section Physical Sensors)
View Full-Text   |   Download PDF [633 KB, uploaded 6 December 2016]   |  

Abstract

The k-nearest neighbour (kNN) rule, which naturally handles the possible non-linearity of data, is introduced to solve the fault detection problem of gas sensor arrays. In traditional fault detection methods based on the kNN rule, the detection process of each new test sample involves all samples in the entire training sample set. Therefore, these methods can be computation intensive in monitoring processes with a large volume of variables and training samples and may be impossible for real-time monitoring. To address this problem, a novel clustering-kNN rule is presented. The landmark-based spectral clustering (LSC) algorithm, which has low computational complexity, is employed to divide the entire training sample set into several clusters. Further, the kNN rule is only conducted in the cluster that is nearest to the test sample; thus, the efficiency of the fault detection methods can be enhanced by reducing the number of training samples involved in the detection process of each test sample. The performance of the proposed clustering-kNN rule is fully verified in numerical simulations with both linear and non-linear models and a real gas sensor array experimental system with different kinds of faults. The results of simulations and experiments demonstrate that the clustering-kNN rule can greatly enhance both the accuracy and efficiency of fault detection methods and provide an excellent solution to reliable and real-time monitoring of gas sensor arrays. View Full-Text
Keywords: gas sensor arrays; fault detection; landmark-based spectral clustering; k-nearest neighbour rule gas sensor arrays; fault detection; landmark-based spectral clustering; k-nearest neighbour rule
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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Yang, J.; Sun, Z.; Chen, Y. Fault Detection Using the Clustering-kNN Rule for Gas Sensor Arrays. Sensors 2016, 16, 2069.

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