Measurement reconstruction in sensor networks for industrial systems

Zhang, Yu, Bingham, Chris and Maleki, Sepehr (2016) Measurement reconstruction in sensor networks for industrial systems. International Journal of Advances in Computer Science & Its Applications, 6 (1). pp. 105-108. ISSN 2250-376X

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Item Type:Article
Item Status:Live Archive


For signal processing in sensor networks there is an on-going challenge for filling missing information when it is either incomplete, uncertain or biased, in ways that are both efficient and with confidence. This paper reviews three established and additional newly developed techniques addressing the problem. Considering sensor signals that are highly correlated in a sensor network, one sensor measurement can be reconstructed based on measurements from other sensors. In such cases, three signal reconstruction methods are considered: 1) principal component analysis (PCA) based missing value approach; 2) self-organizing map neural network (SOMNN) based algorithm; and 3) an analytical optimization (AO) technique. To demonstrate the efficacy of the methods, temperature data are studied on an industrial gas turbine system, where, especially, a faulty sensor signal is utilized to be reconstructed from the other sensor measurements.

Keywords:signal reconstruction, principal component analysis, self-organizing neural network, analytical optimization
Subjects:H Engineering > H321 Turbine Technology
H Engineering > H130 Computer-Aided Engineering
Divisions:College of Science > School of Engineering
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ID Code:31540
Deposited On:04 Apr 2018 11:52

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