The DOI matches its Crossref record.
Automatic choice of measurement locations for dynamic testing
Abstract
This paper examines the problem of choosing an optimum set of measurement locations for experimental modal testing and suggests criteria whereby the suitability of the chosen locations can be assessed. Two methods of coordinate selection are used: one based on Guyan reduction and the other on the Fisher information matrix. Each begins with a detailed finite element model of the structure being tested. Both procedures reduce this model by one degree of freedom at a time until the number of degrees of freedom in the reduced model equals the number of measurement locations required. The choice of the eliminated coordinates is generally automatic, and the coordinates of the reduced model are those used for modal testing
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cited by
Entropy-Based Optimal Sensor Location for Structural Model Updating
Papadimitriou, C., Beck, J. L., & Au, S.-K. (2000) · Journal of Vibration and Control · doi:10.1177/107754630000600508 · Cited by 337
Optimal Sensor Placement for Structural Health Monitoring: A Comprehensive Review
Sun, Z., Mahmoodian, M., Sidiq, A., Jayasinghe, S., Shahrivar, F., & Setunge, S. (2025) · Journal of Sensor and Actuator Networks · doi:10.3390/jsan14020022 · Cited by 53
“The singular value decomposition (SVD) ratio rates the effectiveness of the sensor locations chosen by assessing the ratio between the maximum and minimum of the eigenvector matrix based on the DOFs considered [157].”
Metadata
- Authors
- Penny, J. E. T., Friswell, M. I., & Garvey, S. D.
- Venue
- AIAA Journal
- Published
- 1994-02
- Volume
- 32
- Issue
- 2
- Pages
- 407-414
- ISSN
- 0001-1452 (ISSN-L)
- 1533-385X
- OpenAlex
- W1987169324
- Cited by
- 177
- References
- 0
- FWCI
- 20.73
- Type
- journal article
- Language
- en
- Open access
- closed
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
How to cite
Cite CoChat for the checks on this page, and the paper for its own findings.
- This page
- CoChat. (2026). Citation check: Automatic choice of measurement locations for dynamic testing. https://app.cochat.ai/papers/10.2514/3.11998
- The paper
- Penny, J. E. T., Friswell, M. I., & Garvey, S. D. (1994). Automatic choice of measurement locations for dynamic testing. AIAA Journal. https://doi.org/10.2514/3.11998
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
