Name
Abstract | ||
|---|---|---|
In this paper, a novel approach of sensor placement is proposed for the purpose of maximizing fault detectability and isolability. This new approach rests on the basic fact that faults are embedded in the analytical redundancy relations (ARRs) and that the occurrence of a fault will change the consistency of the corresponding ARRs. Based on these basic facts, the minimal isolating (MI) set is introduced to formulate the full/maximal isolability which is the constraint for sensor placement. Consequently, the optimization problem for sensor placement is reformulated as searching an MI set which is related to the least number of candidate sensors. To find the optimal MI set, a low complexity dynamic programming (LCDP) algorithm is developed on the fault set that consists of system faults and sensor faults. However, sensor faults are varied as different candidate sensors are used. Therefore, another dedicated procedure is proposed to handle this issue. A case study shows that the proposed approach outperforms an existing sensor placement approach in terms of efficiency. |
Year | DOI | Venue |
|---|---|---|
2015 | 10.1109/TASE.2014.2372792 | IEEE Trans. Automation Science and Engineering |
Keywords | Field | DocType |
Vectors,Robot sensing systems,Dynamic programming,Complexity theory,Optimization,Indexes,Fault detection | Dynamic programming,Mathematical optimization,Fault detection and isolation,Computer science,Redundancy (engineering),Optimization problem | Journal |
Volume | Issue | ISSN |
PP | 99 | 1545-5955 |
Citations | PageRank | References |
2 | 0.37 | 19 |
Authors | ||
5 | ||