Search research articles

Search research articles

Related Experiment Video

Updated: Mar 21, 2026

Data Acquisition Protocol for Determining Embedded Sensitivity Functions

Data Acquisition Protocol for Determining Embedded Sensitivity Functions

Published on: April 20, 2016

Data Acquisition Protocol for Determining Embedded Sensitivity Functions.

Janette J Meyer¹, Douglas E Adams², Janene Silvers³

¹Laboratory for Systems Integrity and Reliability (LASIR), Vanderbilt University; janette.j.meyer@vanderbilt.edu.

Journal of Visualized Experiments : Jove

|May 12, 2016

Summary

This summary is machine-generated.

Related Concept Videos

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by

Same author

Evaluation of Impulse Attenuation by Football Helmets in the Frequency Domain.

Journal of biomechanical engineering·2020

Same author

Wind turbine blade shear web disbond detection using rotor blade operational sensing and data analysis.

Philosophical transactions. Series A, Mathematical, physical, and engineering sciences·2015

See all related articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

This study presents a method to find optimal sensor locations for structural health monitoring using embedded sensitivity functions and data from healthy structures. This approach aids in damage detection without needing data from damaged components.

Area of Science:

Engineering
Mechanical Engineering
Materials Science

Background:

Structural health monitoring (SHM) effectiveness relies heavily on sensor and force input placement.
Current algorithms for optimal placement often require data from damaged structures, which is difficult to obtain.
Embedded sensitivity functions offer a novel approach using only healthy structure data.

Purpose of the Study:

To present a data acquisition procedure for determining embedded sensitivity functions.
To outline best practices for calculating these functions.
To demonstrate the method on a residential-scale wind turbine blade.

Main Methods:

Utilizing modal impact testing to acquire frequency response functions.
Calculating embedded sensitivity functions based on healthy structure data.

More Related Videos

Efficient Sampling of Genetically Encoded Biosensor Design Space Enabled with a Design of Experiments and Automation Workflow

Efficient Sampling of Genetically Encoded Biosensor Design Space Enabled with a Design of Experiments and Automation Workflow

Published on: October 17, 2025

Studying Cavitation Enhanced Therapy

Studying Cavitation Enhanced Therapy

Published on: April 9, 2021

Related Experiment Videos

Last Updated: Mar 21, 2026

Data Acquisition Protocol for Determining Embedded Sensitivity Functions

Data Acquisition Protocol for Determining Embedded Sensitivity Functions

Published on: April 20, 2016

Efficient Sampling of Genetically Encoded Biosensor Design Space Enabled with a Design of Experiments and Automation Workflow

Efficient Sampling of Genetically Encoded Biosensor Design Space Enabled with a Design of Experiments and Automation Workflow

Published on: October 17, 2025

Studying Cavitation Enhanced Therapy

Studying Cavitation Enhanced Therapy

Published on: April 9, 2021

Acquiring and analyzing data from a wind turbine blade.

Main Results:

Demonstration of data acquisition for embedded sensitivity functions.
Presentation of representative results from a wind turbine blade.
Inclusion of strategies for evaluating acquired data quality.

Conclusions:

Embedded sensitivity functions enable optimal sensor placement for damage detection using only healthy structure data.
The presented methodology provides a practical approach for SHM.
Effective data acquisition and quality assessment are crucial for reliable SHM.