Name: data acquisition protocol for determining embedded sensitivity functions
Uploaded: 2016-04-20T15:00:00.000+00:00
Duration: 7 min 46 s
Description: Watch this Scientific Journal Video about Data Acquisition Protocol for Determining Embedded Sensitivity Functions at JoVE.com

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/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Optimization+of+location+and+number+of+sensors+for+structural+health+monitoring+using+genetic+algorithm.">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/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Ultrasonic+sensor+placement+optimization+in+structural+health+monitoring+using+evolutionary+strategy.">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/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Multi-objective+optimization+of+sensor+and+excitation+layouts+for+frequency+response+function-based+structural+damage+identification.">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/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Optimal+sensor+placement+for+fault+detection.">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/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=A+Bayesian+approach+to+optimal+sensor+placement+for+structural+health+monitoring+with+application+to+active+sensing.">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/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Sensor+network+optimization+for+a+passive+sensing+impact+detection+technique.">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/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=An+embedded+sensitivity+approach+for+diagnosing+system-level+noise+and+vibration+problems.">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/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Predicting+changes+in+vibration+behavior+using+first-+and+second-order+iterative+embedded+sensitivity+functions.">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/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=A+Damage+Identification+Technique+based+on+Embedded+Sensitivity+Analysis+and+Optimization+Processes.">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/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=A+comparison+of+the+H1,+H2,+and+Hv+frequency+response+functions.">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. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Embedded+Sensitivity+Functions+for+improving+the+effectiveness+of+vibro-acoustic+modulation+and+damage+detection+on+wind+turbine+blades.">Embedded Sensitivity Functions for improving the effectiveness of vibro-acoustic modulation and damage detection on wind turbine blades.</a> , (2014).</li><li>Guratzsch, R., Mahadevan, S. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Structural+health+monitoring+sensor+placement+optimization+under+uncertainty.">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&#39;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 &#34;embedded&#34; in the expressions for the system&#39;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="Equation1" src="/files/ftp_upload/53690/53690equation1.jpg" />
where <img alt="Equation2" src="/files/ftp_upload/53690/53690equation2.jpg" /> is the ESF as a function of frequency, &#969; 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

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...

Watch this Scientific Journal Video about Data Acquisition Protocol for Determining Embedded Sensitivity Functions at JoVE.com

Data Acquisition Protocol for Determining Embedded Sensitivity Functions

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 ...

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

Research

JoVE Journal

Engineering

6.1K Views.  Vanderbilt University. 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.

Video: Data Acquisition Protocol for Determining Embedded Sensitivity Functions

Combining Multiple Data Acquisition Systems to Study Corticospinal Output and Multi-segment Biomechanics

Transcranial magnetic stimulation techniques allow for an in-depth investigation into the neural mechanisms that underpin human behavior. To date, the use of TMS to study human movement, has been limited by the challenges related to precisely timing the delivery of TMS to features of the unfolding movement and, also, by accurately characterizing kinematics and kinetics. To overcome these technical challenges, TMS delivery and acquisition systems should be integrated with an online motion tracking system. The present manuscript details technical innovations that integrate multiple acquisition systems to facilitate and advance the use of TMS to study human movement. Using commercially available software and hardware systems, a step-by-step approach to both the hardware assembly and the software scripts necessary to perform TMS studies triggered by specific features of a movement is provided. The approach is focused on the study of upper limb, planar, multi-joint reaching movements. However, the same integrative system is amenable to a multitude of sophisticated studies of human motor control.

The use of transcranial magnetic stimulation (TMS) to study human motor control requires the integration of data acquisition systems to control TMS delivery and simultaneously record human behavior. The present manuscript provides a detailed methodology for integrating data acquisition systems for the purpose of investigating human movement via TMS.

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Behavior

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Bioengineering

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Neuroscience

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

Metal-organic frameworks (MOFs) are hybrids formed through the coordination of metal ions and organic linkers in organic solvents. The implementation of MOFs in biomedical and industrial applications has led to concerns regarding their safety. Herein, the profile of a selected MOF, a zeolitic imidazole framework, was evaluated upon exposure to human lung epithelial cells. The platform for evaluation was a real-time technique (i.e., electric cell-substrate impedance sensing [ECIS]). This study identifies and discusses some of the deleterious effects of the selected MOF on the exposed cells. Furthermore, this study demonstrates the benefits of using the real-time method versus other biochemical assays for comprehensive cell evaluations. The study concludes that observed changes in cell behavior could hint at possible toxicity induced upon exposure&#160;to MOFs of different physicochemical characteristics and the dosage of those frameworks being used. By understanding changes in cell behavior, one foresees the ability to improve safe-by-design strategies of MOFs to be used for biomedical applications by specifically tailoring their physicochemical characteristics.

Author Spotlight: Advances in Evaluating Human Lung Epithelial Cells' Response to Metal-Organic Frameworks 

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.

Metal-organic frameworks (MOFs) are hybrids formed through the coordination of metal ions and organic linkers in organic solvents. The implementation of ...

Data Acquisition Protocol for Determining Embedded Sensitivity Functions

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