Name: Data Acquisition Protocol for Determining Embedded Sensitivity Functions (Video) | JoVE Journal
Uploaded: 2016-04-20T15:00:00.000+00:00
Duration: 7 min 46 s
Description: Learn to determine embedded sensitivity functions for structural health monitoring using experimental data from a wind turbine blade. This method models structures without requiring specific mass, stiffness, or damping parameters.

ai_agent_IP

ai_agent

maintenance

instantTrialDurationMinutes

isBiopharma

enableNewMyJoveSectionDesign

educationFeatures

seoMetaApi

ai_copilot_institution_blacklist

ai_chat_institution_blacklist

freeAccessWindow

ssoRoleCapture

scheduledMaintenanceBanner

joveLabsFeatures

disable_autoplay_for_bots

extended_trial_duration

5 minutes

The authors have no acknowledgements.

1. Pre-test Preparation
<ol>
	<li>Design and fabricate the test fixture. Design the fixture to replicate realistic boundary conditions by choosing bolt locations to match the mounting locations of the blade. Choose steel for the fixture to minimize the contribution from the fixture to the dynamic response of the test specimen.
	<ol>
		<li>Bolt the blade to the custom t-bracket.</li>
		<li>Clamp the fixture to a steel table.</li>
	</ol>
	</li>
	<li>Identify and mark grid of impact locations.
	<ol>
		<li>Choose 30 points that span the entire blade.</li>
		<li>Mark points with a marker or wax pen and number for reference. Measure point spacing using a tape measure for later use in visual representation of results.</li>
	</ol>
	</li>
	<li>Select and calibrate accelerometers.
	<ol>
		<li>Choose single axis, 10 mV/g accelerometers. Be sure to choose accelerometers with the appropriate sensitivity in order to avoid overloading the sensor and to achieve good signal-to-noise ratios. Also, be sure the frequency range of the sensors is sufficient to capture the frequency range of interest for the test specimen.</li>
		<li>Calibrate each sensor.
		<ol>
			<li>Attach the sensor to a hand-held shaker whose output is a single-frequency force with a magnitude of 9.81 m/sec2 rms (i.e., 1 g).</li>
			<li>Measure the response for 2 sec.</li>
			<li>Determine the rms amplitude of the response from the software readout.</li>
			<li>Multiply the rms amplitude by 1,000 to determine the calibration factor for the accelerometer in units of mV/g.</li>
		</ol>
		</li>
	</ol>
	</li>
	<li>Select hammer and hammer tip.
	<ol>
		<li>Choose an impact hammer with a sensitivity of 11.2 mV/N. Be sure to select a hammer that sufficiently excites the test specimen in both amplitude and frequency range.</li>
		<li>Choose a nylon tip. Be sure to select a hammer tip that sufficiently excites the test specimen in both amplitude and frequency range.</li>
		<li>Connect the hammer to the data acquisition system with a BNC cable.</li>
	</ol>
	</li>
	<li>Identify sensor locations and attach sensors (Figure 4).
	<ol>
		<li>Choose locations at points m and n on either side of the damage location.</li>
		<li>Mount a third accelerometer at location k. Data from this sensor will be used to validate the results of the embedded sensitivity function analysis.</li>
		<li>Attach accelerometers using super glue. Allow the super glue to set completely before conducting the impact testing.</li>
	</ol>
	</li>
	<li>Select test parameters in the data acquisition GUI.
	<ol>
		<li>Enable double hit detection.</li>
		<li>Set the sampling frequency to 25,600 Hz. The usable frequency range is, therefore, 12,800 Hz.</li>
		<li>Set the sample time to 1 sec.</li>
		<li>Select the hammer channel as the trigger channel. Set the trigger level to 10 EU.</li>
		<li>Set the pre-trigger length to 5% of the total sample time. The pre-trigger data is data collected before the data acquisition is started that has been stored in a buffer. It is important to retrieve and save this data so that the entire impact event is captured.</li>
		<li>Select the H1 FRF estimator. This estimator assumes that there is noise on the response channels and no noise on the force channel. 
		Note: Do not window data during acquisition. Windows can be applied in post-processing, if necessary.</li>
		<li>Enter accelerometer and hammer information, including calibration factors and identification notes.</li>
		<li>Save settings for record keeping and for use in future tests.</li>
	</ol>
	</li>
</ol>
2. Impact Testing on the Healthy Blade
<ol>
	<li>Impact point 1 with the hammer. When the amplitude of the impact force exceeds the chosen trigger level, the data acquisition system will be triggered and data, including the selected amount of pre-trigger data, will begin recording.
	<ol>
		<li>During data acquisition, monitor channels to avoid channel clipping and double impacts by observing the time histories displayed in the data acquisition software.</li>
		<li>During data acquisition, monitor the coherence for each accelerometer channel to evaluate the quality of the acquired data by observing the coherence plot in the data acquisition software.</li>
	</ol>
	</li>
	<li>Repeat step 2.1 four more times at point 1.
	<ol>
		<li>Use consistent impact amplitudes for all impacts.</li>
	</ol>
	</li>
	<li>Repeat steps 2.1 and 2.2 for all points.</li>
</ol>
3. Impact Testing on the Damaged Blade
<ol>
	<li>Repeat section 2 on the damaged blade in order to collect data for validating the embedded sensitivity function results. Except for the change in the test specimen, all test parameters are kept the same.</li>
</ol>

<ol><li>Singh, N., Joshi, M. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((Optimization+of+location+and+number+of+sensors+for+structural+health+monitoring+using+genetic+algorithm%5BTitle%5D)+AND+%22Mater+Forum%22%5BJournal%5D)">Optimization of location and number of sensors for structural health monitoring using genetic algorithm.</a> Mater Forum. 33, 359-367 (2009).</li>
<li>Gao, H., Rose, J. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((Ultrasonic+sensor+placement+optimization+in+structural+health+monitoring+using+evolutionary+strategy%5BTitle%5D)+AND+%22Review+Of+Qnde%22%5BJournal%5D)">Ultrasonic sensor placement optimization in structural health monitoring using evolutionary strategy.</a> Review Of Qnde. 25, 1687-1693 (2006).</li>
<li>Raich, A. M., Liszkai, T. R. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((Multi-objective+optimization+of+sensor+and+excitation+layouts+for+frequency+response+function-based+structural+damage+identification%5BTitle%5D)+AND+%22Comput-Aided+Civinfrastructure+Eng%22%5BJournal%5D)">Multi-objective optimization of sensor and excitation layouts for frequency response function-based structural damage identification.</a> Comput-Aided Civinfrastructure Eng. 27 (2), 95-117 (2012).</li>
<li>Worden, K., Burrows, A. P. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((Optimal+sensor+placement+for+fault+detection%5BTitle%5D)+AND+%22Eng+Struct%22%5BJournal%5D)">Optimal sensor placement for fault detection.</a> Eng Struct. 23 (8), 885-901 (2001).</li>
<li>Flynn, E. B., Todd, M. D. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((A+Bayesian+approach+to+optimal+sensor+placement+for+structural+health+monitoring+with+application+to+active+sensing%5BTitle%5D)+AND+%22Mech+Syst+Signal+Pr%22%5BJournal%5D)">A Bayesian approach to optimal sensor placement for structural health monitoring with application to active sensing.</a> Mech Syst Signal Pr. 24 (4), 891-903 (2010).</li>
<li>Markmiller, J., Chang, F. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((Sensor+network+optimization+for+a+passive+sensing+impact+detection+technique%5BTitle%5D)+AND+%22Struct+Health+Monit%22%5BJournal%5D)">Sensor network optimization for a passive sensing impact detection technique.</a> Struct Health Monit. 9 (1), 25-39 (2010).</li>
<li>Yang, C., Adams, D., Yoo, S., Kim, H. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((An+embedded+sensitivity+approach+for+diagnosing+system-level+noise+and+vibration+problems%5BTitle%5D)+AND+%22J.+Sound+Vibration%22%5BJournal%5D)">An embedded sensitivity approach for diagnosing system-level noise and vibration problems.</a> J. Sound Vibration. 269 (3), 1063-1081 (2004).</li>
<li>Yang, C., Adams, D. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((Predicting+changes+in+vibration+behavior+using+first-+and+second-order+iterative+embedded+sensitivity+functions%5BTitle%5D)+AND+%22J.+Sound+Vibration%22%5BJournal%5D)">Predicting changes in vibration behavior using first- and second-order iterative embedded sensitivity functions.</a> J. Sound Vibration. 323 (1), 173-193 (2009).</li>
<li>Yang, C., Adams, D. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((A+Damage+Identification+Technique+based+on+Embedded+Sensitivity+Analysis+and+Optimization+Processes%5BTitle%5D)+AND+%22J.+Sound+Vibration%22%5BJournal%5D)">A Damage Identification Technique based on Embedded Sensitivity Analysis and Optimization Processes.</a> J. Sound Vibration. 333 (14), 3109-3119 (2013).</li>
<li>Rocklin, G. T., Crowley, J., Vold, H. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((A+comparison+of+the+H1%2C+H2%2C+and+Hv+frequency+response+functions%5BTitle%5D)+AND+%22Proc.+Of+IMAC+III%22%5BJournal%5D)">A comparison of the H1, H2, and Hv frequency response functions.</a> Proc. Of IMAC III. 1, 272-278 (1985).</li>
<li>Meyer, J., Adams, D., Silvers, J. Embedded Sensitivity Functions for improving the effectiveness of vibro-acoustic modulation and damage detection on wind turbine blades. ASME 2014 DSCC, , (2014).</li>
<li>Guratzsch, R., Mahadevan, S. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((Structural+health+monitoring+sensor+placement+optimization+under+uncertainty%5BTitle%5D)+AND+%22AIAA+J%22%5BJournal%5D)">Structural health monitoring sensor placement optimization under uncertainty.</a> AIAA J. 48 (7), 1281-1289 (2010).</li>
</ol>

The authors have nothing to disclose.

Test fixtures should be designed to replicate realistic boundary conditions so that results will be applicable under operating conditions. The selection of the number of impact points used for testing is a trade-off between having sufficient spatial resolution and the testing time. Select the hammer based on the size of the test specimen and the frequency range of interest. In general, the smaller the hammer, the broader the frequency range excited. However, smaller hammers typically produce lower amplitude forces. Impac...

Many structural health monitoring techniques rely on changes in measured frequency response functions (FRFs) to detect damage within a structure. However, few of these methods address how to determine sensor placements and/or input force locations that will maximize the effectiveness of the method to detect damage. Embedded sensitivity functions (ESFs) can be used to determine the sensitivity of an FRF to a local change in a structure's material properties. Therefore, because damage typically results in a local change in stiffness, damping, or mass of the structure, ESFs provide a method for determining the best sensor and force locations for FRF-based health monitoring techniques.
The purpose of this video and manuscript is to detail the data acquisition process and best practices for determining ESFs for a structure. The process includes determining various FRFs from modal impact testing, which is performed by exciting a structure with a modal impact hammer and measuring its response with accelerometers. In this work, the structure being tested is a 1.2 m residential-scale wind turbine blade. The goal of the testing and analysis is to identify sensor locations which are most sensitive to damage to the blade. These sensor locations could then be used in a structural health monitoring scheme to monitor the blade for damage.
Besides the use of ESFs to determine the most effective sensor locations to use in a structural health monitoring scheme, several optimal sensor placement algorithms can also be found demonstrated in the literature. In [Kramer], Kramer iteratively evaluates the ability of a set of sensors to observe the modes of a system. More recently, genetic algorithms 1-3 and neural networks 4 have been developed to identify optimal sensor locations. In 5, a Bayesian approach is used that takes into account the risk of different types of errors and the distribution of damage rates. In 6, a finite element model was leveraged to identify the sensor locations most likely to detect damage. In most of the sensor placement algorithms presented in the literature, data from the damaged structure, whether simulated or measured, is required. One advantage of the embedded sensitivity approach is that the sensor locations can be determined from the healthy structure.
Another advantage of ESFs is that material properties need not be explicitly known. Instead, the material properties are "embedded" in the expressions for the system's FRFs. Therefore, all that is needed to calculate ESFs are a set of measured FRFs at particular input/output locations. Specifically, the sensitivity of the FRF (Hjk) calculated from a response measured at point j to an input at point k, to a change in stiffness (Kmn) between points m and n is
<img alt="Equation depicting differential symbol for matrix element calculation in mathematical analysis." src="/files/ftp_upload/53690/53690equation1.jpg" data-altupdatedat="1788235205000" data-alt="Equation1">
where <img alt="Partial derivative formula, ∂H/∂K, mathematical symbol for calculus study." src="/files/ftp_upload/53690/53690equation2.jpg" data-altupdatedat="1788235205000" data-alt="Equation2"> is the ESF as a function of frequency, ω 7-9. The procedure for measuring the FRFs required to calculate the right-hand side of equation (1) is detailed in the next section and demonstrated in the video.

<table><tbody><tr><td>Accelerometer</td><td>PCB</td><td>356B11</td><td>three used in testing</td></tr><tr><td>Impact hammer</td><td>PCB</td><td>086C01</td><td></td></tr><tr><td>Data acquisition card</td><td>NI</td><td>9234</td><td></td></tr><tr><td>DAQ chasis&amp;nbsp;</td><td>NI</td><td>cDAQ-9171</td><td>or similar</td></tr><tr><td>Software</td><td>MATLAB</td><td></td><td></td></tr><tr><td>Super glue</td><td>Loctite</td><td>454</td><td></td></tr><tr><td>Handheld Shaker</td><td>PCB</td><td>394C06</td><td>for calibration&amp;nbsp;</td></tr></tbody></table>

data acquisition protocol for determining embedded sensitivity functions

Data Acquisition Protocol for Determining Embedded Sensitivity Functions

The effectiveness of many structural health monitoring techniques depends on the placement of sensors and the location of input forces. Algorithms for determining optimal sensor and forcing locations typically require data, either simulated or measured, from the damaged structure. Embedded sensitivity functions provide an approach for determining the best available sensor location to detect damage with only data from the healthy structure. In this video and manuscript, the data acquisition procedure and best practices for determining the embedded sensitivity functions of a structure is presented. The frequency response functions used in the calculation of the embedded sensitivity functions are acquired using modal impact testing. Data is acquired and representative results are shown for a residential scale wind turbine blade. Strategies for evaluating the quality of the data being acquired are provided during the demonstration of the data acquisition process.

Figure 1 shows a typical embedded sensitivity function. Similar to an FRF, the ESF has peaks near the natural frequencies of the structure. The higher the value of the ESF, the more sensitive the location is to damage between points m and n. Each of the thirty points tested on the wind turbine blade has a unique ESF. These ESFs can be compared to determine which sensor location would be most sensitive to damage. For example, Figure 2 sho...

The data acquisition procedure for determining embedded sensitivity functions is described. Data is acquired and representative results are shown for a residential scale wind turbine blade.

The effectiveness of many structural health monitoring techniques depends on the placement of sensors and the location of input forces. Algorithms for ...

The overall goal of this procedure is to determine the embedded sensitivity functions of a structure. The procedure will be demonstrated on a residential-scale wind turbine blade. This method can help answer key questions related to structural health monitoring such as how a structure's response will change due to damage at a certain location.The main advantage of this technique is that it provides a method for modeling a structure based on experimentally measured data, eliminating the need for knowing specific parameters for mass, stiffness, and damping. Begin with designing a test fixture to replicate realistic boundary conditions. In this example, bolt locations are prepared to match the mounting locations of the blade.Fabricate the fixture from steel to minimize the fixture's contribution to the dynamic response of the test specimen. Assemble the fixture by first bolting the blade to the custom T-bracket and then clamping the fixture to a steel table. Now, identify and mark a grid of impact locations on the blade.Use a marker or wax pen to mark and number 30 points that span the entire blade. Then, measure out the point's relative position to use for the visual representation of the test results. Next, prepare single-axis 10 millivolts per g accelerometers.Choosing the proper sensitivity of your accelerometer is critical in getting good signal to noise ratios. Also, be sure that the frequency range of the accelerometer is sufficient to capture the frequency range of interest of your specimen. Calibrate each sensor.Attach a sensor to a handheld shaker that can output a single frequency force of 9.81 meters per second squared. Measure the sensor's response for two seconds of shaking. The force output is provided in the software readout.Multiply the RMS amplitude by 1000 to determine the calibration factor for the accelerometer in millivolts per g. The next step is to prepare an impact hammer which has a sensitivity of 11.2 millivolts per newton. Make certain the hammer can excite the test specimen in both amplitude and frequency.Then, attach a nylon tip to the hammer which will not compromise its function. Lastly, connect the hammer to the data acquisition system via a BNC cable. Now, identify sensor locations on the blade and attach the accelerometers with super glue.Choose locations at points m and n on either side of the damaged location. Then, mount a third accelerometer at location k. Data from this sensor will be used to validate the results of the embedded sensitivity function analysis.Open the data acquisition GUI. First, enable double hit detection. Then, set the sampling frequency to 10, 240 hertz.The usable frequency range is half the sampling frequency. Third, set the sample time to one second. Fourth, select the hammer channel as the trigger channel and set the trigger level to 10 newtons.Fifth, set the pre-trigger length to 5%of the total sample time. The pre-trigger data is data collected and stored in a buffer before the data acquisition system is started. It's important to retrieve and save this data so that the entire impact event is captured.Sixth, select the H1 FRF estimator which assumes that there is noise on the response channels and no noise on the force channel. Lastly, enter the accelerometer and hammer information including calibration factors and identification notes. Then, save the settings for record keeping and for use in future tests.Once the super glue used to attach the sensors has fully cured, impact point one with the hammer. When the amplitude of the impact force exceeds the chosen trigger level, the data acquisition system will be triggered and data, including the selected amount of pre-trigger data, will begin recording. During data acquisition, monitor the channels in the software to avoid clipping and double impacts.Also, observe the coherence plot to further evaluate the quality of the acquired data. Do not window the data during acquisition. Impact point one four more times with consistent impact amplitudes.Then, repeat this process for each selected point on the blade. After impacting all the points, repeat the process entirely on the damaged blade. The data from the damaged blade is only needed to evaluate the effectiveness of the embedded sensitivity functions.It is not necessary to determine the embedded sensitivity functions themselves. Similar to frequency response functions, the embedded sensitivity functions have peaks near the natural frequencies of the structure. The higher the value of the functions, the more sensitive the location is to damage between points m and n.Take, for example, the amplitudes of the function near 142 Hertz. It is clear that sensor locations corresponding to the squares in the first and third columns are most sensitive to the damage. Note that these locations were determined from data acquired with a healthy blade.The difference between the frequency response functions determined from the healthy blade and the frequency response functions determined from the damaged blade shows that the embedded sensitivity functions are very effective at predicting the locations on the blade which are most sensitive to damage, as evidenced by the similarity between these two plots. After watching this video, you should have a good understanding of how to acquire the data necessary to determine a structure's embedded sensitivity functions. Although demonstrated on a wind turbine blade, this procedure is applicable to any structure whose response can be measured with an impact hammer and an accelerometer.

data-acquisition-protocol-for-determining-embedded-sensitivity

Research

JoVE Journal

Engineering

The electrophysiological technique of intracellular recording is demonstrated and used to determine spectral sensitivities of single photoreceptor cells in the compound eye of a butterfly.

Determination of Photoreceptor Cell Spectral Sensitivity in an Insect Model from In Vivo Intracellular Recordings

A scanner for imaging magnetic particles in planar samples was developed using the planar frequency mixing magnetic detection technique. The magnetic intermodulation product response from the nonlinear nonhysteretic magnetization of the particles is recorded upon a two-frequency excitation. It can be used to take 2D images of thin biological samples.

Frequency Mixing Magnetic Detection Scanner for Imaging Magnetic Particles in Planar Samples

This work describes a nanoimprinting lithography method to fabricate high-quality sensing arrays that work on the principle of extraordinary optical transmission. The biosensor is low-cost, robust, easy to use, and can detect cardiac troponin I in serum at clinically relevant concentrations (99th percentile cutoff ∼10-400 pg/mL, depending on the assay).

Using Extraordinary Optical Transmission to Quantify Cardiac Biomarkers in Human Serum

This study describes a protocol to measure exposure levels in the 2.4 GHz band, avoiding the uncertainties caused by the use of personal exposimeters as measuring devices. These alterations of the exposure levels should be taken into account, especially in compliance testing, where exposure limits are defined from non-perturbed data.

Effective Analysis of Human Exposure Conditions with Body-worn Dosimeters in the 2.4 GHz Band

The presented experimental protocol can be used to perform real time measurements of cavitation activity in a cell culture device with the aim of enabling investigation of the conditions required for successful drug delivery and/or other bioeffects.

Studying Cavitation Enhanced Therapy

We present a method for simultaneously collecting fMRI and fNIRS signals from the same subjects with whole-head fNIRS coverage. The protocol has been tested with three young adults and can be adapted for data collection for developmental studies and clinical populations.

Simultaneous Data Collection of fMRI and fNIRS Measurements Using a Whole-Head Optode Array and Short-Distance Channels

The following study evaluates the toxicological profile of a selected metal-organic framework utilizing electric cell-substrate impedance sensing (ECIS), a real-time, high-throughput screening technique.

Electric Cell-Substrate Sensing for Real-Time Evaluation of Metal-Organic Framework Toxicological Profiles

Additively manufactured polymers have been widely used for producing elastic metamaterials. The viscoelastic behavior of these polymers at ultrasonic frequencies remains, however, poorly studied. This study reports a protocol to estimate the viscoelastic properties of 3D-printed polymers and show how to use them to analyze the metamaterial dynamics.

Characterizing Dissipative Elastic Metamaterials Produced by Additive Manufacturing

This protocol provides a method for the systematic global optimization of genetically encoded biosensors through automation-assisted genetic library generation and assessment. This is coupled with design-of-experiment methodologies to streamline experimentation and enable the selection of genetic components to tune biosensors to specific design outcomes.

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

A technique for performing quantitative three-dimensional (3D) imaging for a range of fluid flows is presented.  Using concepts from the area of Light Field Imaging, we reconstruct 3D volumes from arrays of images.  Our 3D results span a broad range including velocity fields and multi-phase bubble size distributions.

Determining 3D Flow Fields via Multi-camera Light Field Imaging

Learn to determine embedded sensitivity functions for structural health monitoring using experimental data from a wind turbine blade. This method models structures without requiring specific mass, stiffness, or damping parameters.

Data Acquisition Protocol for Determining Embedded Sensitivity Functions

Explore More Videos