Bayesian optimal sensor placement for parameter estimation under modeling and input uncertainties

Tulay, Ercan,; Papadimitriou, Costas

doi:10.1016/j.jsv.2023.117844

Cited by 5 publications

(3 citation statements)

References 54 publications

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“…Another important alternative approach involves formulating the sensor placement based on the concept of information entropy [85][86][87], which characterises the uncertainty in the modal parameters. The goal is to search for a sensor configuration that minimises the change in information entropy [88].…”

Section: Mode Shape Expansion From Qr-pivot Sensors Placement 61 a Br...mentioning

confidence: 99%

Efficient Modal Identification and Optimal Sensor Placement via Dynamic DIC Measurement and Feature-Based Data Compression

Wang

2023

Vibration

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Full-field non-contact vibration measurements provide a rich dataset for analysing structural dynamics. However, implementing the identification algorithm directly using high-spatial resolution data can be computationally expensive in modal identification. To address this challenge, performing identification in a shape-preserving but lower-dimensional feature space is more feasible. The full-field mode shapes can then be reconstructed from the identified feature mode shapes. This paper discusses two approaches, namely data-dependent and data-independent, for constructing the feature spaces. The applications of these approaches to modal identification on a curved plate are studied, and their performance is compared. In a case study involving a curved plate, it was found that a spatial data compression ratio as low as 1% could be achieved without compromising the integrity of the shape features essential for a full-field modal. Furthermore, the paper explores the optimal point-wise sensor placement using the feature space. It presents an alternative, data-driven method for optimal sensor placement that eliminates the need for a normal model, which is typically required in conventional approaches. Combining a small number of point-wise sensors with the constructed feature space can accurately reconstruct the full-field response. This approach demonstrates a two-step structural health monitoring (SHM) preparation process: offline full-field identification of the structure and the recommended point-wise sensor placement for online long-term monitoring.

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Section: Mode Shape Expansion From Qr-pivot Sensors Placement 61 a Br...mentioning

confidence: 99%

Efficient Modal Identification and Optimal Sensor Placement via Dynamic DIC Measurement and Feature-Based Data Compression

Wang

2023

Vibration

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“…Similarly, previous studies have attempted to apply Bayesian optimal sensor placement to problems in physics and engineering (Verma et al, 2019; Ercan and Papadimitriou, 2023). These studies have also relied on Monte Carlo integration to evaluate the utility function, which the authors note to be extremely computationally expensive.…”

Section: Introductionmentioning

confidence: 98%

Optimal experiment design with adjoint-accelerated Bayesian inference

Yoko,

Juniper

2024

DCE

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We develop and demonstrate a computationally cheap framework to identify optimal experiments for Bayesian inference of physics-based models. We develop the metrics (i) to identify optimal experiments to infer the unknown parameters of a physics-based model, (ii) to identify optimal sensor placements for parameter inference, and (iii) to identify optimal experiments to perform Bayesian model selection. We demonstrate the framework on thermoacoustic instability, which is an industrially relevant problem in aerospace propulsion, where experiments can be prohibitively expensive. By using an existing densely sampled dataset, we identify the most informative experiments and use them to train the physics-based model. The remaining data are used for validation. We show that, although approximate, the proposed framework can significantly reduce the number of experiments required to perform the three inference tasks we have studied. For example, we show that for task (i), we can achieve an acceptable model fit using just 2.5% of the data that were originally collected.

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“…Note that Bayesian A-optimal, , V-optimal, and G-optimal designs have also been used for developing chemical process models but are less popular. Bayesian designs have also been used to aid fundamental model development in related fields. − …”

Section: Introductionmentioning

confidence: 99%

Simple Method for Incorporating Prior Parameter Information during Model-Based D-Optimal Design of Experiments for Multi-Response Chemical Process Models

Gibson,

Liu,

Harris

et al. 2024

Ind. Eng. Chem. Res.

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Fundamental models are important for design, control, and optimization of chemical systems but often contain many unknown parameters that require estimation from experimental data. Model-based design of experiments (MBDoE) can be used to plan experiments, leading to accurate parameter estimates and improved model predictions. Computation of MBDoE objective functions is challenging for nonlinear models with many parameters due to a singular Fisher information matrix. Bayesian approaches can be used to overcome this problem. We propose a simplified Bayesian D-optimal objective function and minimum volume ellipsoid (MVE) calculation to test the effectiveness of the Bayesian approach. A pharmaceutical case study is used to show estimates obtained from sequential Doptimal experiments that occupy a smaller MVE than estimates from old data or corner-point experiments, confirming that the proposed approach leads to improved parameter estimates. Estimates from A-optimal and D-optimal experiments are compared, confirming that estimates from D-optimal experiments are better in a D-optimal sense.

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Bayesian optimal sensor placement for parameter estimation under modeling and input uncertainties

Cited by 5 publications

References 54 publications

Efficient Modal Identification and Optimal Sensor Placement via Dynamic DIC Measurement and Feature-Based Data Compression

Efficient Modal Identification and Optimal Sensor Placement via Dynamic DIC Measurement and Feature-Based Data Compression

Optimal experiment design with adjoint-accelerated Bayesian inference

Simple Method for Incorporating Prior Parameter Information during Model-Based D-Optimal Design of Experiments for Multi-Response Chemical Process Models

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