An enhanced sequential sensor optimization scheme and its application in the system identification of a rail-sleeper-ballast system

•The enhanced sequential sensor placement (ESSP) performs efficient sensor placement•ESSP configurations need not be supersets (or subsets) of any optimal configuration•ESSP can select more than one configuration at placement levels by a novel criterion•The optimal configurations’ alternatives by ES...

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Veröffentlicht in:Mechanical systems and signal processing Jg. 163; S. 108188
Hauptverfasser: Lam, Heung-Fai, Adeagbo, Mujib Olamide
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Berlin Elsevier Ltd 15.01.2022
Elsevier BV
Schlagworte:
Algorithms
Ballasted track
Configurations
Damage detection
Entropy (Information theory)
Hammers
Impact analysis
Information entropy
Model updating
Optimization
Placement
Railroad ballast
Railroad ties
Railway tracks
Redundancy
Sensor configuration
Sensors
Sequential sensor placement
Spatial correlation
System identification
Sensor configuration
Ballasted track
System identification
Information entropy
Spatial correlation
Sequential sensor placement
ISSN:0888-3270, 1096-1216
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Zusammenfassung:•The enhanced sequential sensor placement (ESSP) performs efficient sensor placement•ESSP configurations need not be supersets (or subsets) of any optimal configuration•ESSP can select more than one configuration at placement levels by a novel criterion•The optimal configurations’ alternatives by ESSP cater to unexpected site constraints•Prediction error correlation and minimum sensor spacing mitigate sensor redundancy The problem of optimal sensor placement for system identification and damage detection is addressed by the development of a robust method based on Bayesian theory. Information entropy is used as the optimality measure to select the optimal configuration from the candidate configurations for a given number of sensors. The enhanced sequential sensor placement (ESSP) algorithm was developed to efficiently address the computational bottlenecks that may arise when a large number of measurable degrees of freedom (DOFs) are considered for candidate configurations. In this paper, the sensor redundancy problem in finely meshed models was addressed by considering (1) the spatial correction of prediction errors at measurable DOFs and (2) a minimum sensor interval. The proposed ESSP algorithm and the two strategies for handling sensor redundancy were studied by comparing the optimal configurations from the conventional methods and that from the ESSP algorithm for a rail-sleeper-ballast system. Finally, the optimal sensor configurations thus obtained were verified via model updating of an in-situ ballasted track system using measured data from an impact hammer test. The analysis results clearly show improvements in the optimality of the sensor configuration with the proposed method relative to the conventional methods.
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ISSN:0888-3270
1096-1216
DOI:10.1016/j.ymssp.2021.108188