Please use this identifier to cite or link to this item:
|Title:||Sensor selection and placement using low complexity dynamic programming||Authors:||Chi, Guoyi
|Issue Date:||2012||Source:||Chi, G., Le, T., Wang, D., Yu, M., & Luo, M. (2012).Sensor selection and placement using low complexity dynamic programming. 2012 IEEE Conference on Prognostics and Health Management (PHM), 1-6.||Abstract:||In this paper, a novel approach is proposed for sensor selection and placement in systems for the purpose of fault detection and isolation (FDI). This new approach benefits from the basic fact that faults are embedded in the analytical redundancy relations (ARRs) and that the occurrence of a fault will cause the corresponding ARRs to change. For FDI purposes, each ARR is connected to a set of sensors that represent the measurable variables. New concepts of fault associated sets and fault distinguishable sets are introduced to develop a low complexity dynamic programming algorithm to minimize the number of sensors needed and simultaneously to guarantee all possible faults being detectable and isolable. A case study of a fuel-cell system shows that the proposed method performs well when the numbers of faults and sensors are moderate.||URI:||https://hdl.handle.net/10356/105522
|DOI:||10.1109/ICPHM.2012.6299519||Fulltext Permission:||none||Fulltext Availability:||No Fulltext|
|Appears in Collections:||EEE Conference Papers|
Updated on Jul 16, 2020
Updated on Dec 5, 2020
Items in DR-NTU are protected by copyright, with all rights reserved, unless otherwise indicated.