Optimal sensor placement for state estimation of a thin double-curved shell structure

Weickgenannt, Martin; Neuhaeuser, Stefan; Henke, Benjamin; Sobek, Werner; Sawodny, Oliver

doi:10.1016/j.mechatronics.2013.01.009

Cited by 21 publications

(11 citation statements)

References 16 publications

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“…and a short overview can be found in [4]. Table 1 summarizes some of the different observability criteria which are used in references individually or combined [5,6]. The first column shows the criterion based on which the observability is quantified, the second column shows the mathematical expression of the criterion, while the third column shows, if one is to rank different system configurations based on their observability degree, whether the 'best' configuration would be the maximum or minimum of the criterion in the first column.…”

Section: IImentioning

confidence: 99%

“…The problem of observability quantification has been discussed in many papers, including [2][3][4][5][6][7] to name a few related to the sensor placement. Observability Gramian is one of the observability degree measures used most often.…”

Section: Introductionmentioning

confidence: 99%

“…Through further evaluation of the observer performance, it was deduced in [2] that the 'condition number' (CN) criterion was better suited for sensor placement in a multicomponent distillation and reactive distillation rather than the former two criteria. Although some other references have proposed similar criteria and applied it to sensor and actuator placement of a shell structure [5] or proposed a new measure such as determinant criterion [8], no references have investigated the observability degree for a case study of thermal sensors placement in a battery string.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Observability degree criteria evaluation for temperature observability in a battery string towards optimal thermal sensors placement

Lystianingrum

Hredzak

Agelidis

et al. 2014

2014 IEEE Ninth International Conference on Intelligent Sensors, Sensor Networks and Information Processing (ISSNIP)

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Temperature measurement in battery strings is most critical not just for safety reasons but also to restrain operating conditions that would reduce the battery lifetime. However, placing a thermal sensor in each individual battery cell within a battery string implies cost and space. In this paper, the optimal number and location of thermal sensors in a forced-cooled battery string is discussed. Different observability Gramianbased observability criteria are used to evaluate and compare observability degree of placement of different number and different combinations of sensors locations in the battery string. The observability degree is further verified by evaluating the observer performance for the different sensor locations. The most suitable observability criteria to ascertain the optimal location of such thermal sensors are recommended.

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Section: IImentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Observability degree criteria evaluation for temperature observability in a battery string towards optimal thermal sensors placement

Lystianingrum

Hredzak

Agelidis

et al. 2014

2014 IEEE Ninth International Conference on Intelligent Sensors, Sensor Networks and Information Processing (ISSNIP)

View full text Add to dashboard Cite

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“…High-speed rail [9] Multi Nonlinear Not considered Greedy algorithm Water network [10] Single Nonlinear Considered Bayesian approach Framework [11] Single Nonlinear Considered Bayesian approach Framework [12] Single Nonlinear Considered Stochastic decomposition algorithm Water network [13] Single Nonlinear Considered Bayesian approach, genetic algorithm Framework [14] Single Nonlinear Considered Gradient search method Transport-reaction process [15] Single Nonlinear Considered Genetic algorithm Power distribution system [16] Multi Nonlinear Considered Annealing algorithm Shell structure [17] Multi Nonlinear Considered Hybrid greedy randomized adaptive search procedure Vehicular network [18] Multi Nonlinear Considered Genetic algorithm Framework [19] Most of the studies have a single objective function and do not consider system uncertainties. The highlighted cells in Table 1 identify optimization methodology characteristics that are common with the methodology developed herein.…”

Section: Introductionmentioning

confidence: 99%

An Entropy Based Bayesian Network Framework for System Health Monitoring

Parhizkar

Balali

Mosleh

2018

Entropy

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Oil pipeline network system health monitoring is important primarily due to the high cost of failure consequences. Optimal sensor selection helps provide more effective system health information from the perspective of economic and technical constraints. Optimization models confront different issues. For instance, many oil pipeline system performance models are inherently nonlinear, requiring nonlinear modelling. Optimization also confronts modeling uncertainties. Oil pipeline systems are among the most complicated and uncertain dynamic systems, as they include human elements, complex failure mechanisms, control systems, and most importantly component interactions. In this paper, an entropy-based Bayesian network optimization methodology for sensor selection and placement under uncertainty is developed. Entropy is a commonly used measure of information often been used to characterize uncertainty, particularly to quantify the effectiveness of measured signals of sensors in system health monitoring contexts. The entropy based Bayesian network optimization outlined herein also incorporates the effect that sensor reliability has on system information entropy content, which can also be related to the sensor cost. This approach is developed further by incorporating system information entropy and sensor costs in order to evaluate the performance of sensor combinations. The paper illustrates the approach using a simple oil pipeline network example. The so-called particle swarm optimization algorithm is used to solve the multi-objective optimization model, establishing the Pareto frontier.

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“…Most recently the problem of optimal sensor placement for state estimation and joint state-input estimation has been treated by Weickgenannt at al. [20], van den Linder et al [19] and Zhang and Wang [21]. In [20] an optimal sensor location strategy was developed for an active shell structure.…”

Section: Introductionmentioning

confidence: 99%

Efficient sensor placement for state estimation in structural dynamics

Hernández

2017

Mechanical Systems and Signal Processing

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This paper derives a computationally efficient algorithm to determine optimal sequential sensor placement for state estimation in linear structural systems subjected to unmeasured excitations and noise contaminated measurements. The proposed algorithm is developed within the context of the Kalman filter and it minimizes the variance of the state estimate among all possible sequential sensor locations. The paper investigates the effects of measurement type, covariance matrix partition selection, spatial correlation of excitation and model selection on optimal sensor placement. The paper shows that the sequential approach reaches the optimal sensor placement as the number of sensor increases.

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Optimal sensor placement for state estimation of a thin double-curved shell structure

Cited by 21 publications

References 16 publications

Observability degree criteria evaluation for temperature observability in a battery string towards optimal thermal sensors placement

Observability degree criteria evaluation for temperature observability in a battery string towards optimal thermal sensors placement

An Entropy Based Bayesian Network Framework for System Health Monitoring

Efficient sensor placement for state estimation in structural dynamics

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