Data Acquisition Protocol for Determining Embedded Sensitivity Functions

Summary

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.

Abstract

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.

Introduction

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 (H_jk) calculated from a response measured at point j to an input at point k, to a change in stiffness (K_mn) between points m and n is

where 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.

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Protocol

1. Pre-test Preparation

1. 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.
   1. Bolt the blade to the custom t-bracket.
   2. Clamp the fixture to a steel table.
2. Identify and mark grid of impact locations.
   1. Choose 30 points that span the entire blade.
   2. 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.
3. Select and calibrate accelerometers.
   1. 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.
   2. Calibrate each sensor.
      1. 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).
      2. Measure the response for 2 sec.
      3. Determine the rms amplitude of the response from the software readout.
      4. Multiply the rms amplitude by 1,000 to determine the calibration factor for the accelerometer in units of mV/g.
4. Select hammer and hammer tip.
   1. 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.
   2. Choose a nylon tip. Be sure to select a hammer tip that sufficiently excites the test specimen in both amplitude and frequency range.
   3. Connect the hammer to the data acquisition system with a BNC cable.
5. Identify sensor locations and attach sensors (Figure 4).
   1. Choose locations at points m and n on either side of the damage location.
   2. Mount a third accelerometer at location k. Data from this sensor will be used to validate the results of the embedded sensitivity function analysis.
   3. Attach accelerometers using super glue. Allow the super glue to set completely before conducting the impact testing.
6. Select test parameters in the data acquisition GUI.
   1. Enable double hit detection.
   2. Set the sampling frequency to 25,600 Hz. The usable frequency range is, therefore, 12,800 Hz.
   3. Set the sample time to 1 sec.
   4. Select the hammer channel as the trigger channel. Set the trigger level to 10 EU.
   5. 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.
   6. 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.
   7. Enter accelerometer and hammer information, including calibration factors and identification notes.
   8. Save settings for record keeping and for use in future tests.

2. Impact Testing on the Healthy Blade

1. 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.
   1. During data acquisition, monitor channels to avoid channel clipping and double impacts by observing the time histories displayed in the data acquisition software.
   2. 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.
2. Repeat step 2.1 four more times at point 1.
   1. Use consistent impact amplitudes for all impacts.
3. Repeat steps 2.1 and 2.2 for all points.

3. Impact Testing on the Damaged Blade

1. 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.

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Results

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...

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Discussion

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...

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Materials

Name	Company	Catalog Number	Comments
Accelerometer	PCB	356B11	three used in testing
Impact hammer	PCB	086C01
Data acquisition card	NI	9234
DAQ chasis	NI	cDAQ-9171	or similar
Software	MATLAB
Super glue	Loctite	454
Handheld Shaker	PCB	394C06	for calibration