Name: data acquisition protocol for determining embedded sensitivity functions
Uploaded: 2016-04-20T15:00:00
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...

<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>

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 border="0" cellpadding="0" cellspacing="0" width="573">
	<tbody>
		<tr height="21">
			<td height="21" style="height:21px;width:216px;">Accelerometer</td>
			<td style="width:71px;">PCB</td>
			<td style="width:119px;">356B11</td>
			<td style="width:167px;">three used in testing</td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:216px;">Impact hammer</td>
			<td style="width:71px;">PCB</td>
			<td style="width:119px;">086C01</td>
			<td></td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:216px;">Data acquisition card</td>
			<td style="width:71px;">NI</td>
			<td align="right" style="width:119px;">9234</td>
			<td></td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:216px;">DAQ chasis&#160;</td>
			<td style="width:71px;">NI</td>
			<td style="width:119px;">cDAQ-9171</td>
			<td>or similar</td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:216px;">Software</td>
			<td style="width:71px;">MATLAB</td>
			<td style="width:119px;"></td>
			<td></td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:216px;">Super glue</td>
			<td style="width:71px;">Loctite</td>
			<td align="right" style="width:119px;">454</td>
			<td></td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:216px;">Handheld Shaker</td>
			<td style="width:71px;">PCB</td>
			<td style="width:119px;">394C06</td>
			<td>for calibration&#160;</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 ...

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