National Renewable Energy Laboratory 

Innovation for Our Energy Future 

A national laboratory of the U.S. Department of Energy

Office of Energy Efficiency & Renewable Energy

NREL is operated by Midwest Research Institute ● Battelle Contract No. DE-AC36-99-GO10337 

Smart Sensor System for 

Structural Condition Monitoring 

of Wind Turbines 

May 30, 2002 — April 30, 2006 

M.J. Schulz 

University of Cincinnati 

Cincinnati, Ohio 

M.J. Sundaresan 

North Carolina A&T State University 

Greensboro, North Carolina 

Subcontract Report 

NREL/SR-500-40089 

August 2006 
Smart Sensor System for 

Structural Condition Monitoring 

of Wind Turbines 

May 30, 2002 — April 30, 2006 

M.J. Schulz 

University of Cincinnati 

Cincinnati, Ohio 

M.J. Sundaresan 

North Carolina A&T State University 

Greensboro, North Carolina 

NREL Technical Monitor: A. Laxson 

Prepared under Subcontract No. XCX-2-31214-01 

Subcontract Report 

NREL/SR-500-40089 

August 2006 

National Renewable Energy Laboratory

1617 Cole Boulevard, Golden, Colorado 80401-3393 

303-275-3000 • www.nrel.gov 

Operated for the U.S. Department of Energy 

Office of Energy Efficiency and Renewable Energy 

by Midwest Research Institute • Battelle 

Contract No. DE-AC36-99-GO10337
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Contents 

Executive Summary...................................................................................................................... vii 

1.0 Introduction......................................................................................................................... 1 

2.0 Modeling and Simulation of the Sensor System................................................................. 2 

3.0 Manufacturing and Testing the Structural Neural System.................................................. 7 

3.1 Fabrication of Active Fiber Continuous Sensors ........................................................... 7 

3.2 Modeling of Piezoceramics.......................................................................................... 12 

3.3 Individual Sensor Testing............................................................................................. 13 

3.4 Fatigue Monitoring of a Composite Bar....................................................................... 13 

3.5 Testing the Continuous Sensor..................................................................................... 14 

3.6 The Structural Neural System Processor...................................................................... 18 

3.7 Fatigue Testing............................................................................................................. 19 

3.8 Development of Carbon Nanotube Neurons................................................................ 20 

3.9 Monitoring a Wind Turbine Blade for Damage during Proof Testing......................... 23 

4.0 Prototype Sensor System Development............................................................................ 24 

5.0 Using a Continuous Sensor to Monitor Fatigue Damage ................................................. 25 

5.1 Development of Techniques for Damage Assessment and Prognosis ......................... 26 

6.0 Monitoring the Onset of Buckling .................................................................................... 30 

6.1. Composite Bar Subjected to Buckling ......................................................................... 30 

6.2 Summary ...................................................................................................................... 33 

7.0 Design of the Smart Blade ................................................................................................ 34 

8.0 Conclusions....................................................................................................................... 35 

9.0 References......................................................................................................................... 36 

10.0 Publications....................................................................................................................... 38 

11.0 Undergraduate Student Design Project............................................................................. 42 

12.0 Acknowledgments............................................................................................................. 43 

12.1 Faculty and Staff Working on the Project.................................................................... 43 

12.2 Students Supported....................................................................................................... 43

iii
Figures 

Figure 2.1. Architecture of the SNS .............................................................................................................2

Figure 2.2. Response of the plate due to an impulse at the center of the plate. ...........................................3

Figure 2.3. Simulation of the neuron responses due to an impulse (dirac delta function). ..........................4

Figure 2.4. Firing of the column (Y) neurons...............................................................................................5

Figure 2.5. Individual responses of the column neurons after firing............................................................5

Figure 2.6. Combined output of the column neurons due to an impulse excitation.....................................6

Figure 3.1. PZT actuator preform (a), and acoustic sensor preforms (b). ....................................................7

Figure 3.2. Piston assembly of the mold for casting the AFCS....................................................................8

Figure 3.3. Individual PZT ribbons from CeraNova Corporation. ...............................................................8

Figure 3.4. Teflon mold and three sensors. ..................................................................................................9

Figure 3.5. Mold for casting AFCS showing original and updated design and curing in an oven...............9

Figure 3.6. Curing process for the AFCS. ..................................................................................................10

Figure 3.7. Poling setup showing (a) the fixture with high voltage wires and (b) the oven, charge 

amplifier, voltage source, and fixture. ....................................................................................10

Figure 3.8. Sketch of the components of the AFS. .....................................................................................11

Figure 3.9. The active fiber sensor; (a) an individual sensor and electrodes, and (b) a continuous sensor 

with two nodes. The sensing direction is along the length of the sensor................................11

Figure 3.10. Sensors on a long fiberglass panel to simulate a wind turbine blade.....................................13

Figure 3.11. Responses of the longitudinal (a) and transverse (b) sensors. ...............................................13

Figure 3.12. AE due to initial crack propagation........................................................................................14

Figure 3.13. Unfiltered (a) and filtered (b) signals at final failure.............................................................14

Figure 3.14. Series-continuous sensor on a fiberglass plate.......................................................................15

Figure 3.15. Experimental setup for testing of continuous sensors bonded on a structure. .......................15

Figure 3.16. AE at individual sensors; (a) at sensor 1, and (b) at sensor 2. ...............................................16

Figure 3.17. Response of the two-node continuous sensor. .......................................................................17

Figure 3.18. Charge amplifier circuit. ........................................................................................................18

Figure 3.19. Combined circuit involving both the charge amplifier and the active filter circuit...............19

Figure 3.20. The SNS capturing data in a fatigue test. Only the PC and SNS box are used......................19

Figure 3.21. Beam with a fatigue crack monitored by two single node neurons........................................19

Figure 3.22. The MWNT neuron, continuous strain sensors and its dynamic response in a cantilever ....21

Figure 3.23. The first spray-on piezoresistive CNT neuron on an insulated thin aluminum strip. ............21

Figure 3.24. CNT polymer sensors on a fiberglass beam simulating a wind turbine blade. ......................22

Figure 3.25. A fiberglass beam has been taped and the CNT neuron is being sprayed on. .......................22

iv
Figure 3.26. Schematic of the SNS on the test wind turbine blade ............................................................23

Figure 5.1. Composite specimen with fatigue damage...............................................................................25

Figure 5.2. Test setup for monitoring the damage growth with a continuous sensor.................................25

Figure 5.3. Comparison of AE waveforms obtained by a continuous sensor and conventional sensors. .26

Figure 5.4. AE proof test procedure [15]....................................................................................................27

Figure 5.5. AE energy from composite coupons during initial static loading............................................28

Figure 5.6. Estimation of fatigue durability of woven glass fiber epoxy composite coupon specimens ...28

Figure 6.1. Specimen used for buckling experiments.................................................................................30

Figure 6.2. Instrumentation used for measuring the specimen response....................................................31

Figure 6.3. Comparison of waveforms obtained in the unbuckled and buckled condition; .......................32

Figure 6.4. Response of the buckled bar to chirp excitation. .....................................................................33

Figure 7.1. Initial design for a smart blade.................................................................................................34

v
vi
Executive Summary 

The high cost of energy due to limited resources may be the most critical problem facing the world today, 

and this problem is becoming worse each year. The energy crisis is focusing a lot of attention on 

renewable energy, such as wind and solar energy, which is not likely to run out. In the case of wind 

energy, there is great interest in deploying small and large wind turbines for generating distributed power 

for residential and rural areas. These turbines represent a significant capital investment for the owner and 

the turbine must operate for up to 30 years. Failure of one rotor blade on the turbine could cause 

catastrophic damage and jeopardize the user's investment and confidence in wind energy. Structural 

health monitoring (SHM) of the rotor blades is a solution that can warn of damage to the blade and 

protect the entire horizontal-axis wind turbine from failure. In addition, a condition-based maintenance 

program can be adopted using the health monitoring information. The advantages of structural health 

monitoring and a condition based maintenance strategy are to: 

• Minimize labor costs for inspection of turbines. 

• Prevent unnecessary replacement of components based on time of use. 

• Uncover design weaknesses before failure. 

• Improve the availability of power while preventing overload of the turbine. 

• Allow repair rather than replacement of blades by detecting damage early. 

• Protect the investment in wind power by residential owners, utility companies, and public 

facilities such as schools that are trying to reduce energy costs. 

• Use lighter advanced blades made from fiberglass and graphite that will allow the large turbines 

to react to wind changes more quickly, and thus capture more wind energy. 

The design of the wind turbine blade is an important factor in the performance (power produced) and 

reliability of the wind turbine. The trend in blade design is toward complex blade shapes to improve 

aerodynamic efficiency, and the use of combined fiberglass graphite, and balsa wood materials to 

improve strength and reduce the blade mass moment of inertia that will result in increased “wind 

capture.” In other words, blade geometry and materials are becoming high-tech and complex, and 

structural over-design of the blade is no longer an an acceptable approach. The push to increase wind 

turbine performance and maintain reliability is driving a need for installation of low-cost continuous 

health monitoring systems on wind turbine blades. This system could provide critical information about 

the location and propagation of blade damage. If a blade fails, the rotor can become unbalanced, which 

can destroy the entire turbine. Wind turbine blades made of composites have sudden audible AEs during 

damage growth. This means that instruments can be affixed to the blades to detect and locate these 

emissions to prevent blade failure. Monitoring the blade for damage is also useful in static testing to 

determine at what location the blade starts to fail. This information can be used to improve blade design 

and manufacturing. Wind turbine blades are mostly composite structures with complex geometry, large 

size, and sections that are built of different materials. Any sensor system to be used on the blades must be 

low cost, simple, and operate over decades of use. This makes developing a practical SHM system for 

wind turbine blades a real challenge. 

In this project, the Intelligent Mechanisms Laboratory at North Carolina A&T State University and the 

Smart Materials Nanotechnology Laboratory at the University of Cincinnati collaborated to develop a 

structural neural system (SNS) for SHM of wind turbine blades. The National Renewable Energy 

Laboratory (NREL) sponsored the work and engineers from Sandia National Laboratories provided 

guidance. The tasks that were performed to produce the prototype SNS include: 

• Modeling and simulation of damage in composite materials. 

• Developing continuous sensors for use in the sensor system. 

vii
• Developing the signal processing system. 

• Developing a methodology for buckling health monitoring. 

• Developing damage assessment, prognosis, and life extension techniques. 

• Fabricating a working sensor system. 

• Testing this system on a wind turbine blade at NREL. 

The SNS that was developed is based on AE monitoring, which is a practical approach for low-cost SHM 

of large composite structures such as wind turbine blades. The main advantage of the SNS is that many 

sensor elements can be used with only 2-4 channels of data acquisition. This tremendously simplifies 

SHM. Furthermore, the method is passive, which means no pre-damage data or diagnostic waveforms are 

needed. 

The SNS was tested to identify damage initiation and propagation on a 9-meter-long wind turbine blade 

during a static proof test to failure at the NREL test facility in Boulder, Colorado. The testing was 

performed in January 2006 with the help of the NREL and Sandia Labs staff. Two configurations of the 

SNS were tested. The first configuration used continuous sensors connected to a commercial AE 

monitoring system. The second configuration used continuous sensors connected to the full SNS 

developed in the project. The test instrumentation was brought to NREL from NCA&TSU and the Univ. 

of Cincinnati. Piezoelectric wafers were bonded onto the surface of the wind turbine blade and 

interconnected to form continuous sensors that were connected to the commercial data acquisition 

systems or the SNS processor built in the project. AE data were recorded during the test until the blade 

failed. The testing produced some interesting conclusions. In the beginning of the testing, after the blade 

settled and concurrent AEs quieted, damage initiated in one location on the blade. Then, as the load was 

increased, damage started in a second, third, and fourth location until finally there was a catastrophic 

buckling failure of the blade caused by the graphite spar cap delaminating from the fiberglass shear web 

of the load bearing rib in the blade. The SNS indicated the general area where the damage started and 

indicated how damage progressed, which is valuable information for blade design and for verifying the 

manufacturing technique. Strain gages on the blade did not provide a clear indication of damage until the 

skin buckled. The blade failed at a load above the design load. 

 

The results of laboratory testing and the blade testing at NREL provide confidence that SHM of large 

composite structures that have complex geometry and are made of multiple materials is practical using a 

simple and low-cost SNS. Based on the testing, recommendations to improve the SNS for application on 

wind turbine blades include using a finer grid pattern of the neurons, locating neurons inside the blade at 

critical locations such as the rib and joints, and setting the neuron firing parameters based on the 

structure underneath each neuron to help pinpoint the damage locations. There are also several technical 

upgrades to the hardware of the SNS that could be pursued as subsequent projects to increase the number 

of neurons to monitor large structures, and to have the SNS wirelessly transmit data from a rotating 

system to the fixed frame. In the future, the SNS could also be reconfigured for health monitoring of 

other components on wind turbines, such as bearings and gears. In addition, the architecture of the SNS 

may be modified for measuring other variables, such as the pressure distribution on the blade. This 

information can be used in the control system to improve efficiency and prevent overload and fatigue of 

the blade. This project was highly successful because it developed the SNS, which is a new approach for 

health monitoring of composite structures that is practical and sensitive. We predict that future wind 

turbines will become “smart” and use sensors to diagnose their health and also to define structural 

performance limits that can be fed back to a control system to prevent damage from occurring. The hightech SNS developed for wind turbine blades can also be configured for use on other types of composite 

materials – such as helicopter rotor blades, for example. 

viii
1.0 Introduction 

Wind turbine blades are designed to have lifetimes of up to 30 years. These blades accumulate on the 

order of load cycles and the fatigue life of the blade is an important design consideration. Wind 

turbine blades can become damaged by moisture absorption, fatigue, wind gusts, or lightning strikes. In 

wind farms, aerodynamic interaction between different turbines can cause unpredictable and excessive 

loads on the blades. These excessive loads accelerate fatigue damage to the blade. In addition, normal 

aerodynamic loads and loads that result from changing gravity moments cause fatigue damage to the 

blades. The blades are usually made from fiberglass, which is a cost-effective material for this 

application. However, because of the low specific modulus of fiberglass, the blade natural frequencies 

are low and the deflections of the cantilevered blade can be large. If a fatigue-damaged rotor blade fails, 

it can cause catastrophic damage to the wind turbine and possibly damage nearby structures or injure 

people. Predicting the exact fatigue life of a blade is difficult, and it is difficult to tell the extent of 

fatigue damage that might have occurred to a blade. Thus, manufacturers are seeking a method to 

continuously monitor the condition of the blade and warn of damage or impending failure. 

9 10

Wind turbine blades sometimes fail near the root section or the third of the blade near the root. Buckling 

of the blade's surface at the maximum chord section is one type of failure. The blades may operate for a 

large number of cycles with little reduction in strength and elastic properties, and then damage may 

propagate quickly to failure. Health monitoring of the rotor blades and timely identification of potential 

failure areas can help prevent damage or failure of the horizontal axis wind turbine. A health monitoring 

system that is reliable, low cost, and integrated onto the blade may reduce wind turbine life-cycle costs 

and make wind energy more affordable. The structural health monitoring (SHM) information gathered 

could be used in a condition-based maintenance program that could minimize the time needed for 

inspection of components, prevent unnecessary replacement of components, prevent failures, and allow 

utility companies to be confident of power availability. In addition, SHM may allow the use of lighter 

blades that would provide higher performance with less conservative margins of safety. Furthermore, a 

wind turbine with lighter blades can respond to wind changes more quickly and capture more energy. 

A SHM system to monitor wind turbine blades must be designed to consider thick structural sections, a 

complex built-up structure with multiple curvatures, and the use of a minimum number of signal 

processing channels. Various existing techniques including visual inspection, C-scan, AEs, Lamb wave 

propagation, and shearography can detect damage in composite blades. These techniques are labor 

intensive, or inaccurate, or difficult to use during operation, or require a large number of channels of data 

acquisition and storage of pre-damage data, or accessibility of the blade is required for an extended time. 

In this project, a new highly distributed continuous sensor concept that mimics the signal processing in 

the biological neural system was investigated. This approach can improve the level of sensitivity and lead 

to the development of a smart blade for continuous in-operation damage detection. One of the National 

Renewable Energy Laboratory’s (NREL) concerns in the development of a smart blade is the cost of the 

sensor system. Because the piezoceramic sensor material is used for sensing high-frequency lowamplitude signals, little piezoelectric sensor material is used. The cost of the material and signal 

processing hardware would be justified by the increased reliability of the turbine. The smart blade could 

also provide blade response information to control the turbine performance and to increase the turbine’s 

efficiency. The health monitoring system could monitor each blade on all turbines in a wind farm during 

operation. If damage is indicated, the particular turbine and blade could be identified and this information 

communicated to an operator through a diagnostic monitoring system. A damaged blade could be 

adjusted to reduce loads, or the turbine could be stopped for blade inspection and repair. 

1
2.0 Modeling and Simulation of the Sensor System 

A simulation is performed to aid in design of the sensor system. In the simulation to be described it is 

assumed that continuous sensors are used to form an array. Continuous sensors have multiple nodes 

connected in a series to one channel of data acquisition. The simulation of a 10 x 10 array sensor system 

is performed and the sensor system architecture used mimics that of a Structural Neural System (SNS). 

The SNS is briefly described. It consists of neurons of series-connected piezoceramic (PZT) nodes and is 

one possible concept for the arrangement of continuous sensors. The modeling and details of the 

simulation are given in references [1] and [2]. An impulse load is simulated at the center of a fiberglass 

panel that can represent cracking of fibers or an impact to the blade. The panel used for the simulation is 

1.22 meters (m) square, 0.0064 m thick, and is modeled as being simply supported. A 10-by-10-m SNS 

shown in Figure 2.1 is used. The arrangement of the nodes on the panel is exactly symmetric. At the end 

of each neuron signal, processing electronics are shown as a small box in Figure 2.1. These units produce 

an output voltage for each neuron. The voltages are designated V1 through V20. 

(a) (b) 

Figure 2.1. Architecture of the SNS: (a) Each small square indicates two adjacent sensor nodes. 

There are 200 sensors in (a). Circuits V1 through V20 contain 20 analog output signals from the 

continuous sensors. SNSAP1 and SNSAP2 are two analog processors built to simplify the damage 

location process. T1, C1, T2, and C2 are the four output signals sent to the computer. Software for 

damage detection is written using MATLAB and LABVIEW to acquire and analyze the data; (b) 

Zoomed version of (a) showing detail of arranging sensors to form row continuous sensors and 

column continuous sensors. 

Following is a brief discussion of modeling of the electronic components representing the processing 

functions of the biological nerve cell. A neuron is formed by connecting the PZT sensor nodes in a series. 

A 1-M-ohm resistor signifying the resistance of an oscilloscope was connected to the output of each 

neuron, and each neuron signal is amplified by a factor of 10. A bridge rectifier was simulated by taking 

2
the absolute value of the neuron signal, which produces only a positive rectified signal whenever the 

neuron voltage exceeds a 0.2-volt switching level. The absolute value of the signal, along with an on/off 

switch, models the characteristic of the bridge rectifier. The neurons are assumed to be connected in a 

series for this simulation. A large wave propagation simulation algorithm was developed for this purpose 

and can be used for any number of sensor elements and neurons. The excitation was located at the center 

of the plate. The loading considered in the simulation is an impulse input to represent a very highfrequency excitation, such as an acoustic emission (AE). The wave propagation response of the panel is 

shown in Figure 2.2. The time step used in computing the response is 1 microsecond and 200 vibration 

modes of the panel are used in computing the response solution. The 200th mode natural frequency is 452 

kilohertz (kHz). 

Figure 2.2. Response of the plate due to an impulse at the center of the plate. 

Figure 2.3 shows the response of the plate for a simulated impulse excitation at the center of the simply 

supported glass fiber composite panel. The graph clearly indicates that the sensor is able to detect the 

high-frequency simulated excitation. The algorithm used to simulate the wave propagation assumes a 

linear solution and is efficient for design of the sensor system. The voltage output of all the 20 neurons is 

shown in Figure 2.3, but because of symmetry, half are exactly overlaid on each other. 

3
Figure 2.3. Simulation of the neuron responses due to an impulse (dirac delta function). 

The sequential firing of the 10 column neurons is shown in Figure 2.4. The firing of the row neurons is 

not shown as they fire in the same order as shown for column neurons. The time of firing of the row 

neurons is the same as the column neurons because the plate used is a quasi-isotropic composite plate. In 

the initial part of the response, neurons 5 and 6 (Y-Neurons), and 15 and 16 (X-Neurons) are firing. The 

intersection of these row and column neurons is at the center of the panel. Later in the response the 

voltage output increases because the strain levels in the panel are increasing and because more modes are 

being strained. The output of the neurons adds together and the row and column output voltages increase 

due to the addition of all the neurons that are firing. Note that in the beginning of the response only two 

neurons are firing and later they all are firing. The symmetry in the panel causes the signals to overlap. 

These figures show that the SNS has located the source of an impact at the center of the panel by the 

intersection of the row and columns that are firing. 

Figure 2.5 shows the response of each neuron after firing and indicates that the SNS has detected a very 

high-frequency signal and that the neuron corresponding to the color yellow has fired. The graph clearly 

indicates that a very high-frequency signal was recorded by the SNS and that later on a low-frequency 

signal dominates. The information in Figure 2.5 is actually reduced in the processor and is transparent to 

the user. These signals are combined into one row output and one column output. 

The combined column outputs are shown in Figure 2.6. The column combined output is the signal 

recorded by the signal processing system. The amplitude of the initial part of the column of combined 

outputs also shows the high-frequency content and roughly indicates the level of damage caused by the 

fiber break or the impact. Typically, as damage propagated, the amplitude of the AE signals increased. 

4
Figure 2.4. Firing of the column (Y) neurons. 

Figure 2.5. Individual responses of the column neurons after firing. 

5
Figure 2.6. Combined output of the column neurons due to an impulse excitation. 

There are actually combinations of up to 10 curves in Figure 2.4 and Figure 2.5. The combined row and 

combined column outputs give a composite response that characterizes the frequency and amplitude level 

of the excitation. The SNS presented is an efficient method for parallel processing of structural responses 

and detecting impact and the health of structures. In the response, as the wave propagates, different 

neurons fire and indicate the location of the damage. The combined output signal shows the approximate 

amplitude and frequency content of the AE signal caused by the damage. The electronics hardware to 

achieve a signal processing system similar to that represented in the simulations can be formed using an 

array of neurons in which each neuron has its own Transducer Bus Interface Module (TBIM). The TBIM 

and emulator architecture is described later. An approach to reduce the number of TBIMs needed or to 

further simplify the signal processing architecture needed to process the array data has also been 

developed. This new architecture is called the SNS. The concept for the SNS was submitted as an 

invention disclosure at the University of Cincinnati in collaboration with North Carolina A&T State 

University in 2003. 

6
3.0 Manufacturing and Testing the Structural Neural System 

Damage in fiber composite materials is difficult to detect and the use of composite materials is limited by 

the lack of methods to monitor the condition of structures. There is an increasing need for health 

monitoring techniques that will allow composite materials to be more widely used. Wind turbine 

applications are particularly demanding because they require high-reliability and low-cost composite 

materials. SHM has not been widely used in industry due to the weight, cost, and complexity of the 

added sensors. In order to detect damage, a large number of conventional sensors and data acquisition 

instruments must be used to detect damage. Alternatively, an SNS can be formed using a large number of 

sensor nodes and as few as four channels of data acquisition. This system is configured by a series of 

continuous sensors connected to a data acquisition computer. The continuous sensors are formed by 

individual PZT sensors connected in a series. The density of the sensor nodes determines how sensitive 

the SNS is to detecting minute damage. 

This section discusses the fabrication and testing of continuous active fiber composite (AFC) sensors [2, 

3, 4] for use in predicting failures in composite materials. Continuous sensors are series-connected 

sensors, also called neurons, used to form the SNS. This sensor technology has proven to be effective in 

detecting small damage in structures, such as breaking of composite fibers and delamination. SHM of 

wind turbines is a potential application of continuous sensors because of the large size of the structure. In 

order to detect the position of a failure in structures, unidirectional active fiber sensors built from PZT 

ribbons are considered here. To manufacture the unidirectional sensors, a mold was developed to produce 

thin composites of aligned PZT ribbons. The sensors produced were tested for their ability to detect AEs. 

The sensors were bonded to fiberglass plates and, using pencil lead breaks to simulate AEs in the 

material, limited testing was done cycling the plate in a hydraulic mechanical testing machine to produce 

damage. The results of these experiments are summarized in this section. 

3.1 Fabrication of Active Fiber Continuous Sensors 

The procedure developed to fabricate Active Fiber Continuous Sensors (AFCS) is discussed in the 

following steps (S1 through S6), and in reference [2]. The AFCS is manufactured at the University of 

Cincinnati in collaboration with North Carolina A&T State University. 

(S1). Prepare PZT Ribbon Fibers. The PZT ribbons used for creating the sensors are purchased 

commercially [5]. These ribbons are PZT 5A fibers with an aspect ratio of 3:1. Since they are brittle, they 

can easily be cut with a razor blade to the appropriate length for use in the molding process. The 

manufactured preform and ribbons can be seen in Figure 3.1. 

(a) (b)

Figure 3.1. PZT actuator preform (a), and acoustic sensor preforms (b). 

7
(S2). Molding. The PZT ribbons need to be placed in an epoxy medium in order to remain at equal 

spacing and provide strength to the final wafer. To achieve this, two plates were machined out of 

aluminum and two combs made from key stock were inserted in slots cut in the plate. The combs align 

the PZT fibers in position so that the EPON Resin 862 epoxy can be injected through one of the top 

holes. The injector is made from a stainless steel pipe, which is bored out to 5/16 inner diameter, and an 

aluminum piston, with an o-ring, to force the epoxy out (Figure 3.2). Before being assembled, all pipe 

connections are sealed with Teflon tape. 

Figure 3.2. Piston assembly of the mold for casting the AFCS. 

(S3). Assembly. First, starting with the 61⁄2-inch PZT ribbons shown in Figure 3.3 and described above, 

the ribbons are cut into 3/8-inch pieces using a razor blade. Then, they are cleaned in a bath of acetone 

and heated for 15 minutes at 200o

F so that they will adhere to the epoxy matrix. Next, any part of the 

mold that comes in contact with the epoxy is coated with a release agent and the fibers are aligned in the 

combs and attached to double-sided adhesive tape. Then, the three sensor fibers are attached to the top 

side of the mold, between the injector holes, and the Teflon sheet is placed around them (Figure 3.4). 

Finally, the bottom plate is bolted on with six, 2-inch x 1⁄4-inch - 20 bolts. 

Figure 3.3. Individual PZT ribbons from CeraNova Corporation. 

(S4). Casting. To begin casting, approximately 4.25 grams of EPON Resin 862 epoxy is mixed. Next, 

the hole that the injector is going to be placed into is filled to the bottom of the threads with the epoxy. 

Then, the injector is filled with epoxy and the base of the injector is screwed on. After this, the screw is 

tightened down to remove air from the injector and it is screwed into the hole that was previously filled 

with epoxy. The screw is then tightened until epoxy can be seen filling the opposite hole. This is the 

point where the plug should be inserted and the spring stiffness (k=45.55 pounds per inch [lb/in]) should 

be placed in the injector cylinder. After that, the injector’s screw should be turned 13 full times in order 

to create a pressure of about 350 pounds per square inch (psi). The assembled mold is shown in Figure 

8
3.5 along with an updated design of the mold. Finally, the entire apparatus is set in an oven (Fig. 3.5) at 

200o

F for 2 hours. The curing process is shown below as a plot of temperature vs. time (Figure 3.6). 

After cooling, the thin wafer of PZT ribbons can be removed from the mold by knocking out the holes or 

cutting off the ends just inside the injection holes. Each wafer produces three 1-inch x 1⁄2-inch sensors. 

Figure 3.4. Teflon mold and three sensors. 

Figure 3.5. Mold for casting AFCS showing original and updated design and curing in an oven. 

9
140

0

50

100

150

200

250

300

0 20 40 60 80 100 120

o

F

Figure 3.6. Curing process for the AFCS. 

The new aluminum pressure mold in Figure 3.5 is being used to manufacture the AFCS. This mold 

consists of two parts, Part A and Part B. Care is taken while manufacturing the mold so that the tolerance 

on flatness is maintained at 10 microns. A total of 18 bolts are used to assemble the two parts, which help 

in reducing the gasket thickness and to allow pressurization to improve fiber wetting. The first mold with 

the Teflon gasket did not hold high pressure. 

(S5). Poling. The fibers in the preform are in the unpoled state and they need to be poled to obtain 

electro-mechanical coupling. The extent of poling is a function of time, temperature, and the applied 

field. The bulk coercive field for the PZT 5A material is approximately 11 kilovolts per centimeter 

(kV/cm). This value was used to determine that 700 volts required for poling the sensors. Poling was 

achieved by applying the poling voltage at 200o

F for approximately 20 minutes. The elevated temperature 

helps to align the dipoles in the presence of the strong electric field. The voltage is removed after the 

AFCS has cooled to room temperature. A sensor strip of dimension 1⁄2-inch x 1-inch is cut from the 

preform and sanded carefully until the fibers are exposed. Then the sensor is temporarily electroded on 

both faces by carefully aligning the copper strips of foil to avoid electric short circuiting. A fixture is 

made from Teflon plates for clamping the specimen in place and providing the electrical connection. The 

experimental setup is shown in Figure 3.7. 

Figure 3.7. Poling setup showing (a) the fixture with high voltage wires and (b) the oven, charge 

amplifier, voltage source, and fixture. 

10
(S6). Electroding and Assembly. Fabricating the continuous sensors (Figure 3.8) is done by first 

electroding both sides of a polarized sensor with conductive silver H2OE EPO-TEK epoxy with a strip of 

copper tape protruding, so that it can be attached to the male connector. The silver epoxy is allowed to 

cure in the oven between sides. Next, two strips of heat-curing adhesive Kapton are cut, to cover the 

sensors, and thin ribbons of copper tape deposited on one. Then, other sensors are electroded as before 

and connected in a series with the first one. After that, the top sheet of Kapton is placed over the entire 

network and it is placed in an oven at 200o

F for 90 minutes. The next step is to solder the copper tape, at 

the end of the sensor, into pins and insert them into the connector housing. The Kapton can then be cut to 

shape around the sensor. Figure 3.8 is an individual node with silver deposited on it and when attached in 

a series it forms the continuous sensor. This forms a neuron that functions much like the neuron in the 

human body’s nervous system. A single- and a two-node sensor are shown in Figure 3.9. 

Figure 3.8. Sketch of the components of the AFS. 

(a) (b)

Figure 3.9. The active fiber sensor; (a) an individual sensor and electrodes, and (b) a continuous 

sensor with two nodes. The sensing direction is along the length of the sensor. 

11
3.2 Modeling of Piezoceramics 

To relate mechanical and electrical fields, the piezoelectric constitutive equation is used as: 

 (1) ⎥

⎦

⎤ ⎢

⎣

⎡

⎥

⎦

⎤ ⎢

⎣

⎡ =⎥

⎦

⎤ ⎢

⎣

⎡

T

E

sd

d

S

D

E

t

T ε

The Equation (1) is a coupled field equation that relates the applied electric field in kV/cm, the 

electric displacement in coulombs/m

( ) E x13

( ) D x13

2

, the strain ( ) x16 S , and the stress ( ) T x16 in (MPa). Material 

properties such as the dielectric permittivity ( ) x33

T ε , the induced strain ( ) x63 d , and the compliance ( ) x66E s

are used to relate the mechanical and electrical fields. The subscript means transposed. The superscript 

T represents a constant stress condition, often taken as the unclamped or free mechanical boundary 

conditions. The superscript E represents a constant electric field condition, which is satisfied with shortcircuited electrodes. With the assumption that vibration occurs only in the X-direction and the AFS is 

poled in the Z-direction and using other relations, Equation 1 reduces to: 

t

 . (2) ESeD 3331313

S += ε

Solving for the current: 

 (3) 3ADQi e & & ==

where is the charge on the electrodes, and is the electrode area of the PZT wafer. Substituting 

Equation (2) into Equation (3) gives: 

Q Ae

 . (4) eEASAei 333131

S

e

& & += ε

The permittivity at constant strain is: 

 

e

S ep

A

hC

ε 33 = (5) 

where is the capacitance, is the electrode spacing, and is the electrode area of the PZT. 

Substituting Equation (5) into Equation (4) gives: 

Cp e h Ae

 . (6) e 131 ep EhCSAei 3 & & +=

The voltage field is: 

 . (7) e 3 = / hVE

Substituting Equation (7) into Equation (6) gives: 

 VCSAei . (8) e p & & 131 +=

This shows that the current is proportional to the strain rate and to the rate of change of the voltage 

across the PZT. The PZT material in an electrical circuit acts like a capacitor in which electrons collect 

on the electrode plates but do not flow through the dielectric material. Strain causes a change in 

attraction across the PZT, and electrons collect on the electrodes in proportion to the strain rate of the 

PZT. If i = 0, which simulates an open circuit when voltage measurements are made, then: 

 01

31 VS

C

Ae V

p

e = + . (9) 

The last equation relates the voltage generated in an open circuit by the AFS under dynamic strain 

conditions. In this equation, Ae is the area of the electrode, Vo is the initial voltage, is the induced 

stress constant, and C

31 e

p is the capacitance of the AFS sensor. Equation 3 can be used to find the d31

coefficient by knowing the load and the loading cycle of the mechanical test machine. The voltage 

produced can be monitored by an oscilloscope. Full details of the modeling are given in reference [1]. 

Other references that give information on piezoelectric sensors are [6−14]. This modeling is used in the 

wave propagation simulation algorithm to study sensor designs. The experiments performed to test the 

continuous sensor are described next. 

12
3.3 Individual Sensor Testing 

In order to show unidirectionality of the sensors, this experiment involved two sensors bonded to a 1⁄4-

inch fiberglass sheet where the sensors were perpendicular to each other and independently connected to 

an oscilloscope to record the responses. This setup is shown in Figure 3.10. A 0.7-millimeter (mm) pencil 

lead was broken at the designated point. The lead break acts similarly to a fiber failure in the fiberglass 

plate. Figure 3.11 shows that the sensors are more sensitive to longitudinal waves than transverse waves. 

This is shown by the higher voltage of the longitudinal sensor. The reason for this is that the PZT ribbons 

are bonded in the epoxy matrix in such a way that they become strained more in the longitudinal 

direction than in the transverse direction. This causes a larger charge difference on the longitudinal 

sensor. 

Figure 3.10. Sensors on a long fiberglass panel to simulate a wind turbine blade. 

 

-1 0 1 2 3

x 10-3

-0.4

-0.2

0

0.2

0.4

0.6

0.8

1

Lead break response from logitudinal Sensor

Time (Sec)

Voltage (Volts)

(a) 

-1 -0.5 0 0.5 1 1.5 2 2.5 3

Figure 3.11. Responses of the longitudinal (a) and transverse (b) sensors. 

3.4 Fatigue Monitoring of a Composite Bar 

A fatigue test in a mechanical test machine is described next. For this test, one sensor was bonded to the 

surface of a 1⁄4-inch composite fiberglass sheet. It was then cycled in an MTS machine at 2 Hz until 

failure. Figure 3.12 shows the received AE from initial crack propagation in the composite. This is a 

high-frequency signal that can be seen decaying over time. In Figure 3.13, the signal is the final failure 

AE. This is the point where the crack propagates very quickly and splits the composite board into two 

pieces. The left plot is the unfiltered signal and the right plot is the same signal but passed through a 

high-pass filter. This shows the very high-frequency AE produced at total failure. 

x 1 -3 0

-0.4

-0.3

-0.2

-0.1

0

0.1

0.2

0.3

0.4

0.5

0.6

Lead break response from transverse Sensor

ime (Sec)

Voltage (Volts)

(b)

T

13
0 2 4 6 8 10

x 10-4

-0.1

-0.05

0

0.05

Plot of crack data

Time (Sec)

Voltage (Volts)

Figure 3.12. AE due to initial crack propagation. 

0 1 2 3 4 5

x 10-4

-0.3

-0.2

-0.1

0

0.1

0.2

0.3

0.4

Filtered response of AFS for crack initiation

Time (Sec)

Voltage (Volts)

(b)

0 1 2 3 4 5 6

x 10-4

-0.4

-0.3

-0.2

-0.1

0

0.1

0.2

0.3

0.4

Response of AFS for crack initiation

Time (Sec)

Voltage (Volts)

(a) 

Figure 3.13. Unfiltered (a) and filtered (b) signals at final failure. 

The signals at final failure are important because they show the sensor did not fail due to the high strains 

and the sensor was able to record the very high signal level. The AE that was caused by initial damage 

was detected and would therefore warn of damage so that the wind turbine could be stopped to prevent 

failure, as occurred in Figure 3.13. 

3.5 Testing the Continuous Sensor 

For this experiment the two individual active fiber sensors were bonded to a fiberglass plate with length 

0.5 m, width 0.08 m, and thickness 0.003 m. Node 1 is at 0.14 m from the left edge and node 2 is at 0.34 

m from the same edge. Each sensor node is 0.0127 m long, 0.0254 m wide, and 0.00034 m thick and 

contains 21 ribbon fibers. When connected together in series this produces a continuous sensor as shown 

in Figure 3.14. The resistance and capacitance of the sensor are checked at the beginning and end of the 

testing to verify the sensor properties. All of the plots were produced by breaking a 0.7-mm lead pencil 

on the edge of the plate to produce a longitudinal wave. The lead break is a good simulation of an actual 

14
fiber break. The sensor response to the lead break is checked at the beginning and end of the testing to 

verify that the sensor is bonded properly to the panel. 

Figure 3.14. Series-continuous sensor on a fiberglass plate. 

The experimental setup for testing the active fiber sensors is shown in Figure 3.15. The fiberglass beam 

with the mounted sensors is cycled in three-point bending and the signals from the sensor are filtered and 

then recorded on a digital oscilloscope. 

Figure 3.15. Experimental setup for testing of continuous sensors bonded on a structure. 

For the first part of the experiment, the sensors are disconnected from being in a series and connected to 

individual channels of the oscilloscope. In Figure 3.16 it can be seen that sensor 1, closest to the AE, 

records the signal before sensor 2, the farther one. The time difference between the two recorded signals 

was about 53 microseconds. By knowing the time difference and the 0.20-m spacing between the nodes, 

15
we can find the velocity of the wave, which is then used to find the elastic modulus of the fiberglass 

plate. If the modulus changes, this is another way to indicate damage. 

-1.5 -1 -0.5 0 0.5 1 1.5 2 2.5 3 3.5

x 10-4

-0.2

-0.15

-0.1

-0.05

0

0.05

0.1

0.15

0.2

Plot of Simulated Acoustic Emission Response from Node 1

Time (Sec)

Voltage (Volts)

Symmetric(extensional) part of waves 

AntiSymmetric(Flexural) part 

(a) 

-1 0 1 2 3

x 10-4

-0.06

-0.04

-0.02

0

0.02

0.04

0.06

0.08

Plot of Simulated Acoustic Emission Response from Node 2

Time (Sec)

Voltage (Volts)

Symmetric(extensional) Part 

(b) Antisymmetric (Flexural) Part 

Figure 3.16. AE at individual sensors; (a) at sensor 1, and (b) at sensor 2. 

For the second part of the experiment, the two sensors are reconnected in a series and a similar lead break 

was performed. In Figure 3.17, the different wave components can be identified as the wave travels some 

16
distance. This is used to determine the required spacing of the sensors and to set the filtering frequency 

of the signal processing unit. A multiple-node continuous sensor is used as a neuron in the neural system 

and the response is used to identify and locate damage in the SNS. 

-4 -2 0 2 4 6 8 10 12 14

x 10-5

-0.15

-0.1

-0.05

0

0.05

0.1

0.15

Plot of Simulated Acoustic Emission Data

Time (Sec)

Voltage (Volts)

Figure 3.17. Response of the two-node continuous sensor. 

Continuous sensors using PZT ribbons can detect minute AEs in structures. The advantages of the 

continuous sensor compared to other sensors are that it can have many closely spaced sensor nodes and 

thus becomes more sensitive, it may be used in complex geometries, and can be placed close to the 

damage. The PZT can be molded to different shapes depending on the application. The rectangular 

ribbon fibers in the epoxy make the sensor rugged and conformable. The methods we have developed 

allow creation of flat planar sensors, which detect damage along plates and shell structures. The silver 

epoxy is used to bond directly to the fiber/epoxy sensor surface and to the structure or the Kapton, and a 

silver epoxy lead is used to connect to the copper foil electrodes. This provides improved strain transfer 

and increases sensor voltage. 

The responses received from the AFS were reproducible and had a good signal-to-noise ratio. Continuous 

sensors allow the damage location to be determined by filtering and using a computer with only one 

channel of data acquisition. The fibers make the sensor mostly a unidirectional sensor, which is 

advantageous in locating AEs in some circumstances. An important result of testing the AFC sensor was 

to show that possible shear lag and the very narrow sensor (two fibers wide) reduced the low-frequency 

components of the signals. Testing the Active Fiber Continuous Sensor in a fatigue test of a beam 

showed the sensor has good sensitivity and is rugged. AEs were detected when a seeded crack began to 

propagate. The frequency of occurrences and the amplitude of the emissions increased up to failure of the 

fiberglass bar. An array sensor system with two rows and two columns was also built and tested on a 

composite panel (results are not shown). This testing used four channels of data acquisition and the 

computer was used to represent the functions of the neural system, including firing the neurons. This 

array sensor was able to detect simulated AEs using a lead break anywhere in the panel. 

17
3.6 The Structural Neural System Processor 

The previous work produced an active fiber-composite design of the continuous sensor. The continuous 

sensor can also be formed by connecting monolithic wafers in series. The next step was to develop the 

signal processing architecture of the SNS by mimicking the biological neural system using electronic 

logic circuits. The biomimetic SNS was developed to provide health monitoring capability using a 

passive-signal processing architecture and continuous sensors. A two-neuron SNS was constructed first. 

Testing the functions of the neural system including firing of the neurons was done and the two-sensor 

system was able to detect simulated AEs using a lead break, and it identified which neuron the signal was 

coming from. The next step was to expand to a four-neuron system. Details of the design and testing of 

the SNS processors are given in references [16−21]. The neural system processor includes charge 

amplifiers, comparators, an active filter, and other components. Since the charge produced by an AE 

sensor is very small, the amplifier electronics are needed to amplify the output voltage of the sensor. A 

charge amplifier is used that amplifies the voltage output based on the feedback capacitance (C2) shown 

in Figure 3.18. The output of this circuit is represented by V=Q/C2. This equation proves that the output 

voltage is dependent on the feedback capacitance C2 and not on the sensor’s capacitance (C1) or the 

cable capacitance (not shown in the circuit diagram). The OPA627 op-amp shown in Figure 3.18 was 

obtained from Texas Instruments and is used in the SNS processor circuit. This op-amp has a low input 

bias current and is used typically for piezoelectric sensor applications. 

C2

R1

C1

R8

V 3

2

7 4

6

1

5 +

-

V+ V- OUT

OS1

OS2

Q(t)

Figure 3.18. Charge amplifier circuit. 

The output of the charge amplifier is sent to an active filter to reduce the unwanted low frequencies in the 

circuit. Figure 3.19 shows the combined circuit involving both the charge amplifier and the active filter 

circuit that is the front end of the electronic neuron that mimics the signal processing in the biological 

neuron. The TL071CP op-amps were used for active filter circuit. Input bias current for the active filter 

circuit is typically between 60 and 200 picoamperes. The output impedance of the charge amplifier is 

very small and hence a high current exists at the output of the charge amplifier. The TL071CP can 

therefore be used as opposed to OPA627, which has been used for the charge amplifier circuit. The 

filtering design is an important part of the SNS for AE sensing. Filter bandwidth and gain should be 

adjusted for specific types of structures and materials [19]. Design of the control panel for the SNS was 

done using LABVIEW and is described in reference [22]. Processing the acquired data using LABVIEW 

was done using MATLAB software. 

18
R6

R2 C3

3

2

7 4

6

1

5 +

-

V+ V- OUT

OS1

OS2

3

2

7 4

6

1

5 +

-

V+ V- OUT

OS1

OS2

R5

C6

R1

C4

Q(t)

R4

3

2

7 4

6

1

5 +

-

V+ V- OUT

OS1

OS2

C5

R3

R7

C2

C1

Figure 3.19. Combined circuit involving both the charge amplifier and the active filter circuit. 

3.7 Fatigue Testing 

A two-neuron SNS using aluminum beam specimens with PZT monolithic sensors was tested (see Figure 

3.20 and Figure 3.21). Aluminum is used here to test the sensitivity of the SNS. 

Four point 

bending 

fixture 

SNS box

PC Data acquisition 

system 

(chassis on floor)

Kron Hite filter 

(not part of SNS)

MTS controllers 

Aluminum or 

FG plate 

Figure 3.20. The SNS capturing data in a fatigue test. Only the PC and SNS box are used. 

0 0.2 0.4 0.6 0.8 1 1.2

x 10-4

-0.8

-0.6

-0.4

-0.2

0

0.2

0.4

0.6

0.8

Figure 3.21. Beam with a fatigue crack monitored by two single node neurons. The raw AE signal 

is shown. In larger structures, the neurons will be continuous fibers. 

19
This testing showed that the AE signal level is much lower in aluminum than in composites. When the 

sensor is close to the crack, the AE level is detectable. A Damage Modeling and Prognostics Method is 

being developed to equate AE data to damage. See equations 1-2: 

 ( ) ( )

( ) 022;2

2

2;2

2

2 += 0 += 0 o ≥−

A

A

E

E aaa

ad

dC

C

aa

ad

dC

C

a

 (1, 2) 

where are the cumulative AE events, the cumulative AE amplitudes from the sensors, and the 

predicted crack length, respectively. The use of damage prognostics with the biomimetic SNS is just 

being developed and should be verified by experimentation on different types of structures. Some initial 

work was done testing an aluminum joint using a two-neuron SNS. There will be joints in the wind 

turbine where fatigue can occur. (There is very little information in the literature on health monitoring of 

joints.) It was found that the AE level is much lower in the aluminum joint than in the fiberglass used in 

our typical testing. This indicates the need for a long continuous sensor to be close to any possible 

damage site. 

AE ,, aCC

3.8 Development of Carbon Nanotube Neurons 

A novel idea for a new type of continuous sensor was tested in a preliminary way in this project. A 

carbon nanotube (CNT) film (buckypaper) was produced by solution casting CNTs dispersed in a solvent 

and bonding the film onto a small aluminum beam. A fine crack was developed in a beam and the beam 

was cycled to propagate the crack. The nanotube film sensor produced a sinusoidal signal based on the 

piezoresistive property in which the resistance changes with strain. When the fine crack passed through 

the film sensor the electrical circuit became open and the sensor signal became zero. This is a very 

simple state sensor (on/off) that might be useful in SHM of components with high-feature density where 

wave propagation is difficult. The SNS is a generic sensor system architecture and different types of 

sensors, including the piezoresistive sensors, can be used. The buckypaper film sensors have some 

disadvantages. The film is brittle and difficult to apply on large structures. Therefore, we developed a 

new method to form the sensor. 

The general CNT neuron sensor is basically a long continuous-strain sensor that can be used in the SNS. 

The SNS can use multiple neurons and can monitor strain (using CNT neurons) or crack propagation 

(using piezoelectric ribbon neuron) in a structure in real time. The new CNT neuron was fabricated as a 

long film that was sprayed on the surface of a composite beam to simulate a wind turbine blade. The 

CNT neuron can be any shape including a grid that is attached onto a structure that functions like the 

neural system in the human body. The high cost would be a factor for large structures using a single wall 

carbon nanotube (SWNT) based neuron sensor. Therefore we are investigating building the SNS using 

multi-wall carbon nanotubes (MWCNT) and carbon nanofibers (CNF) instead of SWCNT. The CNF 

have lower electrical conductivity than SWCNT, and show less strain sensitivity. The sensing properties 

of CNF are still being investigated for SHM, but the CNF can reduce the cost in large structural 

applications such as wind turbines. Figure 3.22a shows a nanotube neuron formed using polymethyl 

methacrylate (PMMA) and 10 weight percent MWCNT. The neuron is about 15 cm in length. The CNT 

neuron is actually bonded onto a capton film that in turn is bonded onto the aluminum beam. An epoxy 

coat is used to cover and protect the neuron. The CNT neuron is connected to a wheatstone bridge circuit 

and to an amplifier, filter, and an oscilloscope. The setup is very similar to a strain gage measurement 

system. Figure 3.22b shows the free vibration response of the CNT neuron due to an initial displacement 

(1 cm) of the free end of the beam. Figure 3.22(c) shows the free vibration response of the CNT neuron 

due to an impact on the free end of the beam. The response of the beam shows that the neuron can be 

used as a distributed strain sensor. The CNT neuron might be thought of as many strain gages connected 

in a series. If there is damage (e.g., high strain due to cracking or delamination) anywhere along the 

20
length and underneath the sensor, the strain will increase and be detectable by the change in electrical 

resistance. The electrical resistance is the integration of the resistance along the length of the neuron. 

Thus, a large change in resistance in one local area of the sensor will be detectable. The exact location of 

the damage along the neuron cannot be determined by the resistance measurement alone. Multiple 

neurons in a grid pattern or several neurons in a local area can be used to locate the damage. 

(a)

(b) (c)

Figure 3.22. The MWNT neuron, continuous strain sensors and its dynamic response in a 

cantilever: (a) PMMA-MWCNT 10% wt neuron (150 mm x 4 mm x 0.08 mm) on an aluminum bar; (b) 

free vibration response due to an initial displacement (1 cm) of the free end of the beam; and (c) 

free vibration response due to an impact on the free end of the beam. 

Significant recent progress was made in developing nanotube continuous sensors that mimic biological 

neurons. Different compositions of carbon nanotubes/nanofibers and polymer were fabricated to tune the 

sensitivity of the neuron. Also, a spray-on neuron has been initially tried as shown in Figure 3.23. The 

spray-on neurons may become a great approach for monitoring large structures such as wind turbines. 

Figure 3.23. The first spray-on piezoresistive CNT neuron on an insulated thin aluminum strip. 

Uniform dispersion of the nanotubes to obtain conductivity at the material’s percolation level is a 

problem that we are working on using several approaches. Different compositions of nanotube sensor are 

shown being tested in Figure 3.24. 

21
Figure 3.24. CNT polymer sensors on a fiberglass beam simulating a wind turbine blade. 

A CNT neuron was sprayed onto a long fiberglass beam simulating a wind turbine blade as shown in 

Figure 3.25. The resistance of the neuron was high due to the length. Different methods of fabricating the 

neuron are being investigated. Piezoresistive sensing is used to detect damage using a low-frequency 

(≈100 Hz) data acquisition system.

Fiberglass 

beam 

Template for the 

nanotube film 

sensor 

Figure 3.25. A fiberglass beam has been taped and the CNT neuron is being sprayed on. 

The piezoresistive sensing property (resistance changes with strain) of CNT composites is used as a 

sensitive crack and corrosion sensor. Cracking and corrosion can be sensed independently using the same 

sensor by changes in electrical resistance and capacitance. Strain sensing requires a grid of fine neurons 

(a sensor film) wherein damage breaks the neuron and opens the circuit. For corrosion monitoring, a 50x 

change in double-layer capacitance of a neuron occurs at the beginning of corrosion (electrolyte in 

contact with the neuron). An SHM technique is being developed in which a CNT solution is sprayed onto 

a structure to form a structural neuron to detect cracking, delamination, and corrosion. 

With the recent good results obtained testing the CNT neuron, there appear to be two sensor types that 

can be used in the SNS, the piezoelectric AE sensor, and the piezoresistive CNT distributed strain sensor. 

The CNT sensor is simpler but may not have as high sensitivity as the piezoelectric neuron. Also, the 

signal processing for the neuron will be similar but have different filters and firing levels. Our opinion is 

that these two sensor types used within the SNS will provide a sensitive, cost effective, and practical 

approach for SHM of large structures. 

22
3.9 Monitoring a Wind Turbine Blade for Damage during Proof Testing 

SHM proof testing of a wind turbine blade was performed at NREL in January 2006. A 12-node fourchannel SNS was placed on top of the blade. The SNS detected damage early and multiple damage sites 

on the blade were identified well before the catastrophic failure of the blade. Strain gages on the blade 

indicated damage at various locations just before buckling failure. The damage location predictions using 

the SNS are shown in Figure 3.26 and were approximately verified based on the locations of strain gages. 

Continuous sensors were also placed on the top and bottom of the blade and signals were recorded 

independent of the SNS. The continuous sensors were connected to a commercial AE monitoring system. 

The continuous sensors detected more AEs than the SNS due to the lower settings for signal acquisition. 

The continuous sensors successfully detected damage early and tracked the AE occurrences during the 

damage growth. Details of the testing are given in reference [23]. 

The results of this test demonstrated that the SNS is a viable method for SHM of complex composite 

structures. We suggest that future work include testing a larger SNS on a wind turbine blade during 

fatigue testing, and to test the SNS on other components of the wind turbine.

3B 

1A 1B 

4 

2 

5 

Figure 3.26. Schematic of the SNS on the test wind turbine blade. The SNS has 4 neurons 

identified as V1, V2, V3, and V4. Damage was predicted in locations 1 through 5. 

23
4.0 Prototype Sensor System Development 

A dedicated local processor is a key component in the SHM system. A local processor architecture was 

designed to work with the continuous sensor. This processor, different than the SNS, is designed with a 

local storage of sensor data and a digital bus to download the data. Development of the software emulator 

for the local processor was done in this project. The emulator work was funded by leveraging a grant to 

North Carolina A&T State University from the Air Force Materials Laboratory. A semiconductor 

development company, Triad Semiconductor assisted in the work. 

Two different versions of this emulator were successfully tested to monitor fatigue cracks in complex 

joints in metallic structures. Based on the availability of future funds it is planned to develop the local 

processor chip based on this architecture. Such a chip can autonomously acquire and analyze AE signals, 

and to communicate the reduced data over a digital bus. It is possible to embed or surface mount this 

local processor chip close to the continuous sensor on the structure being monitored. The development of 

this chip involves several steps before the processor could be miniaturized to a level where it can be 

integrated into the structure. 

24
5.0 Using a Continuous Sensor to Monitor Fatigue Damage 

The continuous sensor performance was analyzed and validated using simulated AE signals, namely 

those caused by pencil lead breaks. The advantage of the continuous sensor configuration for monitoring 

actual AE signals generated by fatigue damage growth in a composite specimen was determined. We 

used 610-mm-long, 25-mm-wide, and 3-mm-thick fiberglass cloth laminate specimens. The central 

section of the specimen had two semicircular notches of 0.25-inch radii. The specimen was initially 

subjected to fatigue until a visible crack appeared at one of the edges. The arrangement of sensors on the 

specimen is shown in Figure 5.1. This specimen was instrumented with a surface-bonded continuous 

sensor as shown in Figure 5.1. The first node of the continuous sensor was at a distance of 0.625 inches 

from the fatigue damage site. In addition, two conventional sensors were also attached to the specimen. 

The sensors S1 and S2 were 0.25-inch damped ultrasonic sensors with a resonant frequency of 5 MHz. 

These sensors were chosen for their suitability in terms of wide band non-resonant response needed for 

quantifying AE signals, based on an earlier study. 

Figure 5.1. Composite specimen with fatigue damage. 

The specimen was subjected to a fatigue loading with a mean load of 750 lbs and amplitude of 500 lbs. 

The test setup is shown in Figure 5.2.

Figure 5.2. Test setup for monitoring the damage growth with a continuous sensor. 

25
Figure 5.3 shows the waveforms of the AE signals obtained during the fatigue loading. The amplitudes of 

the signals from the continuous sensor were much larger than the signals from the conventional sensor. 

These differences can be attributed to the frequency responses of the two types of sensors, their relative 

sizes, and to the fact that surface bonded or embedded sensors are likely to be more sensitive to the AE 

signals compared to the conventional sensors. The AE signal sensed by the conventional sensors s2 is 

much smaller in amplitude compared to that from an identical sensor s1, which is merely 3 inches closer 

to the damage site. Furthermore, the high-frequency components present in the signal from s1 are absent 

in the signals from the sensor s2. This variation in the signal characteristics over as short a distance as 3 

inches in composite media illustrates the need for multiple sensor nodes in critical regions for the 

accurate detection of damage magnitudes and rates. 

Figure 5.3. Comparison of AE waveforms obtained by a 

continuous sensor and conventional sensors. 

Panels with continuous sensors were developed for verification of the SHM technique. The work 

included the development of loading fixtures, panels, and the sensor configurations. Glass cloth/polyester 

composite panels with central circular holes were prepared. A 6-mm slit at the end of one of the 

horizontal diameters was cut to initiate localized fatigue damage in these panels. These panels were 

subjected to fatigue loading until a fatigue crack on the order of 0.5 mm was initiated at the end of the 

notch. These panels were all instrumented with surface mounted continuous sensors. 

5.1 Development of Techniques for Damage Assessment and Prognosis 

AE-based proof testing on an initial group of about 30 glass cloth/polyester composite coupon specimens 

was performed. The procedure included the following steps: 

1. Determine the cyclic load amplitude under which high-quality specimen lasts at least a million 

cycles. 

2. In a group of specimens that include high-quality specimens as well as defective specimens, 

apply the proof test procedure developed earlier, and from the results of the proof test it may be 

possible to separate out defective specimens from the good quality specimens even before the 

specimens are subjected to extensive fatigue loading. 

3. Subject all the specimens to the fatigue cycling determine the actual fatigue durability of each 

specimen. 

4. Find the correlation between the predictions of the AE proof test and the actual fatigue 

durability. 

26
For the 30 specimens that were tested in the initial tests, it was found that the maximum cyclic load of 

60% of static strength was too high and resulted in premature failure of even good-quality specimens. 

The undamaged coupons failed in the range of about 5,000 to 35,000 cycles. The limited AE data 

obtained from these specimens did show some trends indicating the usefulness of AE data. However, the 

experiments had to be repeated at lower cyclic load amplitude with additional specimens and it was 

found that the load amplitude had to be reduced to 48% of the static strength for the specimens to last an 

average of one million cycles. These experiments for established the AE-based proof test procedure for 

prediction of the durability of blade materials. 

In an earlier study [15] the AE technique was used with reasonable success for predicting the fatigue 

durability of carbon fiber thermoplastic hip prostheses. The technique involved tailoring the AE 

monitoring technique to capture the crucial damage-related events occurring during the fatigue 

degradation of the composite structural member. Specifically, the AE was recorded during the proof test 

shown in Figure 5.4. The fatigue life for these specimens varied over 3 decades, and from the AE data 

obtained prior to the fatigue loading it was possible to differentiate the good composite specimens from 

those that failed prematurely in the fatigue loading. 

This technique is now being reapplied for the prediction of fatigue durability of composite coupon 

specimens using distributed sensors. Figure 5.5 shows the cumulative AE energy recorded during the 

initial static loading of the coupon specimens. After the initial static loading to 50% of the nominal static 

strength, the specimens were cyclically loaded for 10,000 cycles to a maximum stress of 50% of the 

static strength. At the end of this cyclical load, the specimen was subjected to a proof load reaching a 

maximum of 60% of static load. AE signals during this stage were recorded. Finally, the specimens were 

loaded in fatigue at 60% of nominal static strength until failure. 

Figure 5.4. AE proof test procedure [15]. 

27
Figure 5.5. AE energy from composite coupons during initial static loading. 

103104105 103

104

105

106

107

108

109

No of cycles to failure

Cumulative AE Energy

Figure 5.6. Estimation of fatigue durability of woven glass fiber epoxy composite coupon 

specimens; blue circles represent specimens used for establishing the trend of AE energy vs. life; 

the five pink triangles represent the specimens whose fatigue life was estimated at 50% of static 

strength; red squares represent the actual fatigue life of these five specimens at reduced fatigue 

load (40% of static strength instead of 50% of static strength). 

28
The relationship between the AE parameters and fatigue durability was established. Based on the life 

estimations from this data, fatigue load amplitudes could be suitably modified to extend the coupon lives 

to desired lengths. During the reported period, additional coupons were tested to validate the trend in the 

life extensions. 

The relationship between the AE energy generated during a proof testing procedure that precedes the 

fatigue testing and the fatigue life subsequently measured for nominally similar specimens has been 

established. Some of these specimens were machined specimens while others had two different levels of 

impact damage. Specimens in each category had orders of magnitude variability in fatigue life and the 

three categories overlapped in terms of their fatigue durability. This test program involved developing the 

proof testing procedure, testing nearly 50 specimens both through the proof tests and fatigue durability 

tests. As shown in figure 5.6, reasonably good correlation between the total AE energy during the proof 

tests and the fatigue life is seen, considering that both the life and AE energy span over 3 orders of 

magnitude. The specimens used to generate the correlation between the AE energy and the fatigue life 

are shown as blue circles. The next step is to make use of this new information that can be used to 

estimate the fatigue durability of nominally similar specimens. There are two ways in which this 

information could be potentially useful. 

• One of the important difficulties in designing structural members for fatigue life is that the 

design is driven by the weakest members of the population and hence is over conservative. Any 

information on the estimated life of individual members can reduce the conservatism and thus 

make the systems lighter and less expensive. 

• We can improve the survivability of a part after it has been damaged. If an individual turbine 

blade is estimated to have shorter fatigue life under the design loads, it may be possible to reduce 

the operating load by a small fraction (often only 5 to 10% reduction is required) to make it 

survive the designed life. This approach to life extension is currently being studied. Five more 

specimens were subjected to impact damages and taken through the proof testing procedure to 

estimate their fatigue life. With the intention of extending the life of weaker specimens, they 

were subjected to reduced fatigue loads. These specimens were subjected to 40% of static 

strength instead of the 50% of static strength that was used to generate the AE Energy versus 

Life trend until they failed. 

This testing has shown that in a laboratory setting it is possible to estimate the life of coupons subjected 

to varying degrees of damage, and use this information to extend their life through appropriate reduction 

in their operating load so that the coupons survive for design life. 

29
6.0 Monitoring the Onset of Buckling 

Experiments were carried out to detect incipient buckling in simple laboratory experiments. These 

experiments were the continuation of earlier wind turbine blade tests at the National Wind Energy 

Center, when the potential of using the stress wave propagation technique for detecting incipient 

buckling was identified. These experiments are described below. 

 

6.1. Composite Bar Subjected to Buckling 

The experiments described in this section used vibration/stress wave monitoring for the detection of 

buckling under more controlled conditions. A composite bar 2 inches wide and 0.125 inch thick was 

instrumented with two PZT patches as shown in Figure 6.1. This specimen was first supported in the 

straight unbuckled condition as shown. The ends were clamped using 0.25 x 2.5 x 6-inch plates. The left 

end of the bar was kept stationary while the right end of the plate was translated to introduce the desired 

level of buckling. The central buckling displacement was measured using a ruler. Rubber sheets were 

introduced between the metal plates at the clamped ends so as to nearly eliminate the waves from being 

reflected at the ends. The clamping force was accurately controlled using torque wrenches. The clamping 

steel plates at each end were held by five individual steel bolts and each of these bolts was tightened to 

the same level of torque (15 ft-lbs) as the bar was taken through different levels of buckling. The 

instrumentation used for introducing vibration into the specimen and for monitoring the amplitude of the 

vibrations is shown in Figure 6.2. It consisted of an arbitrary function generator and a power amplifier, 

which delivered gated sine pulses to the transmitting PZT patch on the bar. The signal from the receiving 

PZT patch was collected directly in a digital oscilloscope and transferred to the personal computer (PC). 

In the first set of experiments, various exciting frequencies ranging from 40 Hz to 15 kHz were 

examined. One of the frequencies that provided good indications of the buckling deformation was 72 Hz. 

The exciting pulse was a Guasian sine pulse consisting of two complete cycles of sine waves at 71 Hz 

created in an arbitrary function generator with an amplitude of 9 volts. This pulse was amplified by a 

power amplifier by a factor of 20 before being applied to the PZT patch on the left side of the specimen. 

Figure 6.1. Specimen used for buckling experiments. 

30
Figure 6.2. Instrumentation used for measuring the specimen response. 

The signals from the receiving PZT patch were recorded when the buckling induced deflection at the mid 

point of the bar was 0.2 inch (0.5 cm), 0.4 inch (1.0 cm), 0.69 inch (1.75 cm), and 1.0 inch (2.5 cm). 

Finally, the bar was returned to its original unbuckled state and the vibration amplitude was again 

measured. Figure 6.3 shows the comparison of the waveforms corresponding to the unbuckled state and 

various buckled states. The magnitude of the oscillations undergoes a drastic reduction as the buckling 

displacement increases. These changes are apparent even at the early stages of buckling when the central 

displacement is of the order of 0.25 inch. When the bar was returned to the unbuckled state, the signal 

amplitude returned to the initial unbuckled bar amplitude as seen from Figure 6.3, indicating that the 

changes in amplitude are indeed caused by the buckling deformation in the bar. In a second experiment, 

the bar was excited by sinusoidal chirp signals with various starting and ending frequencies. The results 

corresponding to a 3-second chirp with a starting frequency of 60 Hz and ending frequency of 120 Hz is 

shown in Figure 6.4. 

It is possible to estimate the resonant frequencies of the bar in the different stages of buckling from the 

horizontal positions of the peaks in the response curves. The frequency of oscillation at time t = 0, 1, 2, 

and 3 seconds are respectively 60, 80, 100, and 120 Hz. In general it could be seen that as the buckling 

deformation increases, the frequency of maximum amplitude for the bar, which originally was at about 

76 Hz, gradually increased until it reached 112 Hz. However, when the displacement of mid point of the 

bar increased from 0.0 to 0.2 inch (0.5 cm), the resonant frequency appeared to fall to about 68 Hz. As 

the buckling displacement increased from this value on, the general trend described above was followed. 

31
(a) 

(b) 

Figure 6.3. Comparison of waveforms obtained in the unbuckled and buckled condition; 

(a) the complete waveforms, and (b) the initial portion of the waveforms expanded. 

32
Figure 6.4. Response of the buckled bar to chirp excitation. 

6.2 Summary 

In this section a vibration-based technique for detecting the onset of buckling in bars is described. Since 

buckling can lead to damage to the materials as well as catastrophic failure of the structure, the technique 

described is likely to be of interest in health monitoring applications. The simple experiment described 

indicates that it is possible to remotely detect even early stages of buckling-related deformation. Further 

study of the interaction between the vibration characteristics and buckling deformation is necessary to 

formalize this technique and is being pursued. 

33
7.0 Design of the Smart Blade 

The smart blade could be coupled to a remote monitoring station that would log health information from 

the wind turbine. An approach has also been outlined to make the intelligent blade autonomous. The PZT 

nerves are already self-powered, and the AFC material can also be used for micro power generation to 

power the TBIM for signal processing. Wireless transmission of the reduced health information could 

simplify the health monitoring system. It is estimated that signal processing electronics on each blade 

might require on the order of 10 watts of power to operate. This could be achieved by harvesting strain 

energy in the blades produced by the gravity moments and the wind loading. The improved reliability, 

reduced maintenance, and increased wind capture would make the smart blade cost effective. The 

concept for the smart blade is shown in Figure 7.1. 

Figure 7.1. Initial design for a smart blade. 

34
8.0 Conclusions 

Modeling, simulation, and experimentation have shown the feasibility of detecting damage in composite 

structures using continuous sensors in different arrangements. The algorithms developed in this work are 

based on detecting damage using a minimum number of channels of data acquisition. In developing the 

algorithms, we considered the cost, the large size of the blades, and the fact that the system must operate 

for 30 years. The test results obtained in this project indicate that health monitoring using continuous 

sensors is a practical technique for monitoring the condition of large structures. Vibration, AEs, and 

strains can be measured using the distributed sensors. The development of a smart blade that uses 

continuous sensors and microelectronics to mimic the biological nervous system is the eventual goal of 

the research. Testing of continuous sensors showed that they were capable of detecting AEs on large 

composite structures and simultaneously reducing the number of channels of data acquisition. The sensor 

system for the smart blade was developed in this project. It is hoped that a prototype smart blade can be 

built and tested with collaboration of NREL, a blade manufacturer, and Sandia National Laboratory. 

35
9.0 References 

1. Kirikera, G., An Artificial Neural System for Structural Health Monitoring. MS Thesis, Cincinnati, 

OH: University of Cincinnati, August 2003. 

2. Datta, S., An Active Fiber Continuous Sensor for Structural Health Monitoring. MS Thesis, 

Cincinnati, OH: University of Cincinnati, August 2003. 

3. Sundaresan, M.J.; Schulz, M.J.; Ghoshal, A.; Pratap, P. “A Neural System for Structural Health 

Monitoring,” SPIE 8th International Symposium on Smart Materials and Structures, San Diego, CA, 

March 4−8, 2001. 

4. Bent, AA; Hagood, N.W “Piezoelectric fiber composite with Interdigitated Electrodes,” J. Intell. 

Mater. Syst. Struct., Vol. 8, 1998. 

5. CeraNova Corporation, 101 Constitution Boulevard, Suite D, Franklin, MA 02038-2587. Online 

www.ceranova.com/products.htm, 2006. 

6. Wilkie, W.K.; Bryant, R.G.; High, J.W.; Fox, R.L. Hellbaum, R.F. Jalink, A.; Little, B.D.; Mirick, 

P.H. “Low Cost Piezocomposite Actuator for Structural Control Applications.” SPIE 7th Annual 

International Symposium on Smart Structures and Materials, Newport Beach, CA, March 5−9, 2000. 

7. Martin, W.N.; Ghoshal, A.; Schultz, M.J.; Sundaresan, M. J. “Structural Health Monitoring Using an 

Artificial Neural System,” Recent Research Developments in Sound and Vibrations. Transworld 

Research Network, 2002. 

8. Schulz, M.J.; Chattopadhyay, S.; Sundaresan, M.J; Ghoshal, A.; Martin, W.N.; Pratap, P.R.; 

“Processing, Understanding and Applications of Piezoceramic Materials for an Artificial Neural 

System.” Ceramic Materials Symposium at the International Mechanical Engineering Congress and 

Exposition, Winter Annual Meeting of the ASME, New York, NY, Nov. 11−16, 2001. 

9. Cady, W.G. Piezoelectricity: an introduction to the theory and applications of electromechanical 

phenomenon in crystals. Dover, NY, 1964. 

10. Kirikera, G;Shultz, M.J.; Ghoshal, A.; Sundaresan, M.J.; Feaster, J.; Hughes, D. “Recent Advances 

in an Artificial Neural System for Structural Health Monitoring,” SPIE 10th International Symposium 

on Smart Materials and Structures, San Diego, CA, March 2−6, 2003. 

11. Datta, S.; Kirikera, G.R.; Pammi, S.; Brown, C.; Schulz, M.J.; Sundaresan, M.J. “Continuous 

Sensors for Structural Health Monitoring,” SPIE 10th International Symposium, NDE for Health 

Monitoring and Diagnostics, San Diego, CA, March 2−6, 2003. 

12. Muralt, P., Piezoelectric Thin Films for MEMS, Laboratoire de Ceramique, Federal Institute of 

Technology, Lausanne, Switzerland, 1997. 

13. Wan, J.G.; Tao, B.Q. “Design and Study on a 1-3 Anisotropic Piezocomposite Sensor,” Nanjing 

University, Nanjing, PR China, Materials and Design 21, 2001. 

14. Ko, W. H. “The Future of Sensor and Actuator Systems,” Case Western Reserve University, 

Cleveland, OH, Sensors and Actuators A56. 

15. Sundaresan, M. J.; Henneke, II, E.G.; Reifsnider, K.L. 1994, “Prediction of Fatigue Life of 

Composite Femoral Prostheses Using Acoustic Emission Technique,” Journal of Composites - 

Technology and Research, pp. 127−137. 

36
16. Kirikera, G.; Datta, S.; Westheider, B.; Schulz, M.J.; Sundaresan, M.J.; Martin, Jr., W.N.; Ghoshal, 

A., “An Artificial Neural System,” Invention Disclosure, University of Cincinnati, UC 103-046. 

August 26, 2003. 

17. Datta, S.; Hause, J.; Kirikeria, G.; Hurd, D.; Schulz, M.J.; Sundaresan, M., “An Active Fiber 

Continuous Sensor,” Invention Disclosure, University of Cincinnati, UC 103-034. July 21, 2003. 

18. Kang, I.; Lee, J.W.; Yeo-Heung, Y.; Shanov, V.; Schulz, M.J.; “A Multifunctional Carbon Nanotube 

Neuron for Structural Health Monitoring,” Invention Disclosure, University of Cincinnati, UC 105-

046. Prepared July 2005. Submitted July 29, 2005. 

19. Kirikera, G.; A Structural Neural System for Health Monitoring of Complex Structures, Ph.D. 

Dissertation, University of Cincinnati, Cincinnati, Ohio, 2006. 

20. Kirikera, G.R.; Shinde, V.; Schulz, M.J.; Ghoshal, A.; Sundaresan, M.J.; Allemang, R.J.; Lee, J.W. 

“A Structural Neural System for Real-Time Health Monitoring of Composite Materials,”, Submitted 

for publication February 2006 to Structural Health Monitoring: An International Journal. 

21. Kirikera, G.R.; Shinde, V.; Schulz, M.J., Allemang, R.; Sundaresan, M.J.; Ghoshal, A. “A Structural 

Neural System for Health Monitoring of Composite and Metallic Structures under Ambient 

Conditions,” Mechanical Systems and Signal Processing. 

22. Shinde, V. Development of Active Fiber Sensors and a Data Acquisition Module for a Structural 

Neural System. MS Thesis. Cincinnati, OH: University of Cincinnati, 2006. 

23. Kirikera, G.R.; Schulz, M.J.; Sundaresan, M.J.; Hughes, S.; Gevorgian, V.; VanDam, J.; Nkrumah, 

F.; Grandhi, G. “Structural Health Monitoring Proof Testing of a Wind Turbine Blade at the National 

Renewable Energy Laboratory.” To be submitted for publication. 

37
10.0 Publications 

The following papers have been submitted or published thus far with partial support by the project. 

(a) Papers Submitted: 

1. Lee, J.W.; Kirikera, G.R.; Kang, I.; Schulz, M.J.; Shanov, V.N. “Structural Health Monitoring using 

Continuous Sensors and Neural Network Analysis.” Submitted June 9, 2005 to Smart Materials and 

Structures. In review. 

2. Inpil Kang, Mark J. Schulz, Jay H. Kim, Vesselin Shanov, Donglu Shi, A Carbon Nanotube Strain 

Sensor for Structural Health Monitoring, Volume 15, Number 3, June 2006, Smart Materials and 

Structures.

3. Kirikera, G.R.; Shinde, V.; Schulz, M.J.; Allemang, R.; Sundaresan, M.J.; Ghoshal, A.; “A Structural 

Neural System for Health Monitoring of Composite and Metallic Structures under Ambient 

Conditions.” Accepted, Mechanical Systems and Signal Processing. 

4. Kang, I.; Heung, Y.Y.; Kim, J.H.; Lee, J.W.; Gollapudi, R.; Subramaniam, S.; Narasimhadevara, S.; 

Hurd, D.; Kirikera, G.R.; Shanov, V.; Schulz, M.J.; Shi, D.; Boerio, J.F.; Mall, S.; Ruggles-Wren, 

M.; “Introduction to Carbon Nanotube and Nanofiber Smart Materials,” Composites B Journal, 37 

2006. 

5. Elwasia, N.; Sundaresan, M.J.; Schulz, M.J.; Ghoshal, A.; Pai, P.F.; Tu, P.K.C. “Damage Bounding 

Structural Health Monitoring,” Journal of Intelligent Materials and Smart Structures, in press. 

(b) Papers published in peer-reviewed journals: 

1. Martin Jr., W. N.; Ghoshal, A.; Sundaresan, M. J.; Lebby, G.; Pratap, P.R.; Schulz, M. J. “An 

Artificial Neural Receptor System for Structural Health Monitoring,” Structural Health Monitoring: 

An International Journal; Vol. 3, No 3, 2005. 

2. Hughes D. R.; Ghoshal A.; Rowe, E.; Sundaresan, M.J.; Schulz, M.J.; Feaster, J.T. “Health 

Monitoring of Thick Materials Using Piezoceramic Patches, Time signals and Wavelet 

Transmittance Functions,” J. of Shock and Vibration; Vol. 11, No. 1, 2004, pp. 47−66. 

3. Schulz, M.J.; Sundaresan, M.; McMichael, J.; Clayton, D.; Sadler, R.; Nagel, W.; “Piezoelectric 

Materials at Elevated Temperature,” J. Intelligent Material Systems and Structures; Vol. 14, No 11, 

November 2003, pp. 693−705. 

4. Schulz, M.J.; Ghoshal, A.; Sundaresan, M.J.; Pai, P.F.; Chung, J.H. “Theory of Damage Detection 

using Constrained Vibration Deflection Shapes,” Structural Health Monitoring: An International 

Journal; Vol. 2, No. 1, March 2003. 

5. Sundaresan, M.J.; Ghoshal, A.; Lia, J.; Schulz, M.J.; Pai, P.F.; Chung, J.H.; “Experimental Damage 

Detection on a Wing Panel Using Vibration Deflection Shapes,” Structural Health Monitoring: An 

International Journal, Vol. 2, No. 3, 2003. 

38
(c) Papers published in non-peer reviewed journals or conference proceedings: 

1. Kirikera, G.; Shinde, V.; Kang, I.;Schulz, M.J.; Lee, J.W.; Ghoshal, A.; Sundaresan, M.J. “A Dual 

Mode Active/Passive Structural Neural System.” International Workshop on Structural Health 

Monitoring, Stanford, CA: Stanford University, September 12−14, 2005.

2. Kirikera, G.R.; Kang, I.P.; Lee, J.W.; Shinde, V.; Westheider, B.; Shanov, V.; Schulz, M.J.; 

Sundaresan, M.; Ghoshal, A. “Testing the Analog Processor of a Structural Neural System.” SPIE 

Smart Structures Conference, San Diego, CA, March 6−10, 2005. 

3. Shinde, V.; Kirikera, G.; Schulz, M.; Datta, S.; Westheider, B.; Hurd, D.; Hause, J.; Gilliland, R.; 

Sundaresan, M.J. “An Experimental Structural Neural System,” Eleventh International Conference 

on Composites/Nano Engineering, ICCE-11 August 8−14, 2004 in Hilton-Head Island, South 

Carolina, USA. 

4. Sundaresan, M.; Kemerling, J.; Nkrumah, F.; Grandhi, G.; Schulz, M.J. “Evaluation Of A Scalable 

Structural Health Monitoring System Based on Acoustic Emission Sensing,” European Workshop on 

SHM, Munich Germany, July 7−9, 2004. 

5. Sundaresan; Nkrumah, F.; Grandhi, G.; Schulz, M. “Identification of Failure Modes Using 

Continuous AE Sensor System,” Proceedings of the 2004 International Mechanical Engineering 

Congress and Exposition, Anaheim, CA, American Society of Mechanical Engineers, New York, 

NY. 

6. Ali, B.; Sundaresan, M.; and Schulz, M. “Early Detection of Local Buckling in Structural Members,” 

ASME International Mechanical Engineering Congress, Anaheim, CA, November 13−19, 2004.

7. Sundaresan, M.; Grandhi, G.; Schulz, M.J.; Kemerling, J.C. “Monitoring Damage Growth in a 

Composite Plate Using a Continuous Sensor.” January 26−29, 2004, Dearborn, MI, USA.

8. Kirikera, G.R.; Datta, S.; Schulz, M.J.; Ghoshal, A.; Sundaresan, M.J. “Mimicking the Biological 

Neural System Using Active Fiber Continuous Sensors and Electronic Logic Circuits,” SPIE 11th

International Symposium on Smart Structures, San Diego, CA, March 2004.

9. Sundaresan, M.J.; Grandhi, G.; Uitenham, L.; Schulz, M.J.; Kemerling, J.; Hughes, J.; 

“Development of an Acoustic Emission Based Structural Health Monitoring System,” Fourth 

International Workshop on Structural Health Monitoring, September 15−17, 2003, Stanford, CA: 

Stanford University. 

10. Sundaresan, M.J.; Grandhi, G.; Schulz, M.J. “A Structural Health Monitoring System Based On 

Continuous Acoustic Emission Sensors,” Quantitive Nondestructive Evaluation Conference, Green 

Bay, Wisconsin, July 27−August 1, 2003. 

11. Kirikera, G.; Datta, S.; Hause, J.; Westheider, B.; Schulz, M.J.; Ghoshal, A.; Sundaresan, M.J.; 

Pratap, P. “Mimicking the Biological Neural System,” ICCE-10 Conference, New Orleans, LA, July 

20−26, 2003. 

12. Datta, S.; Hause, J.; Kirikera, G.; Hurd, D.; Schulz, M.J.; Sundaresan, M.J.; Ghoshal, M.J. “A 

Continuous Sensor for Ultrasensitive Acoustic Monitoring,” ICCE-10 Conference, New Orleans, LA, 

July 20−26, 2003. 

13. Pammi, S.L.; Brown, C.; Kang, I.; Jain, S.; Hurd, D.; Narasimhadevara, S.; Mokashi, V.; Liu, Y.; 

Schulz, M.J.; Chase, J.; Kaul, P.;He, P.; Yang, L.; Xiao, F.; Shi, D.; Boerio, J.; “Multifunctional 

Materials using Carbon Nanotubes,” ICCE-10 Conference, New Orleans, LA, July 20−26, 2003. 

39
14. Grandhi, G.; Sundaresan, M.; Schulz, M.; “Monitoring of Damage Evolution in Composite 

Laminates with Distributed Sensors,” ICCE-10 Conference, New Orleans, LA, July 20−26, 2003. 

15. Ali, B.; Sundaresan, M.; Gurunathan, S.; Ferguson, F.; Schulz, M. “Early Detection of Structural 

Instability,” ICCE-10 Conference, New Orleans, LA, July 20−26, 2003.

16. Elwasila, N.; Sundaresan, M.; Schulz, M.; Sankar, J. “A Damage Vector Method for Structural 

Health Monitoring of Bars and Beams,” ICCE-10 Conference, New Orleans, LA, July 20−26, 2003. 

17. Datta, S.; Kirikera, G.; Schulz, M.J.; Ghoshal, A.; Sundaresan, M.J. “An Active Fiber Continuous 

Sensor,” Fourth International Workshop on Structural Health Monitoring, September 15−17, 2003, 

Stanford, CA: Stanford University. 

18. Kirikera, G.; Datta, S.; Schulz, M.J.; Ghoshal, A.; Sundaresan, M.J. “An Artificial Central Nervous 

System for Structural Health Monitoring of Orthotropic Materials,” Fourth International Workshop 

on Structural Health Monitoring, September 15−17, 2003, Stanford, CA: Stanford University. 

19. Ghoshal, A.; Prosser, W.H.; Kirikera, G.; Schulz, M.J.; Hughes, D.; Orisamolu, W. “Concepts and 

Development of Bio-Inspired Distributed Embedded Wired/Wireless Sensor Array Architectures for 

Acoustic Wave Sensing in Integrated Aerospace Vehicles,” Fourth International Workshop on 

Structural Health Monitoring, September 15−17, 2003, Stanford, CA: Stanford University. 

20. Sundaresan, M.; Ali, B.; Uitenham, L.; Ferguson, F.; Schulz, M.J.; “Development of Health 

Monitoring Techniques for the Detection of Incipient Buckling In Aerospace Structures,” Fourth 

International Workshop on Structural Health Monitoring, September 15−17, 2003, Stanford, CA: 

Stanford University. 

21. Sundaresan, M.; Uitenham, L.; Grandhi, G.; Uppaluri, S.; Schulz, M.J. “Evaluation of Failure Modes 

and Fatigue Durability of Structural Members Using Embedded Instrumentation.” Fourth 

International Workshop on Structural Health Monitoring, September 15−17, 2003, Stanford, CA: 

Stanford University. 

22. Datta, S.; Kirikera, G.; Sundaresan, M.J.; Schulz, M.J. “Active Fiber Composite Continuous 

Sensors,” International Conference on Smart Materials, Structures and System (ISSS-SPIE 2002), 

December 12−14, 2002, Bangalore, India. 

23. Sundaresan, M.; Grandhi, G.; Schulz, M.; Kirikera, G. “Embedded Continuous Sensor for 

Monitoring Damage Evolution in Composite Materials,” SPIE Conference on Smart Structures, 

Devices, and Systems, RMIT University, December 16−18, 2002, Melbourne, Australia. 

24. Sundaresan, M.; Ali, B.; Ferguson, F.; Schulz, M.J.“Early Detection of Local Buckling In Composite 

Bars,” SPIE Conference on Smart Structures, Devices, and Systems, RMIT University, December 

16−18, 2002, Melbourne, Australia. 

25. Pammi, S.; Brown, C.; Datta, S; Kirikera, G.R.; Schulz, M.J.; Kaul, P.; He, P.; Shi, D.; Boerio, F.J.; . 

Sundaresan, M.J. “Building Artificial Nerves for Structures,” IMECE2002-33475, Proceedings of 

IMECE’02 2002 ASME International Mechanical Engineering Congress & Exposition, New Orleans, 

LA, November 17−22, 2002. 

26. Schulz, M.J.; Sundaresan, M.J.; McMichael, J.; Clayton, D. “Evaluation of the Performance of 

Piezoceramic Materials at Moderately Elevated Temperatures,” IMECE2002-34376, Proceedings of 

IMECE’02 2002 ASME International Mechanical Engineering Congress & Exposition, New Orleans, 

LA, November 17−22, 2002. 

40
27. Sundaresan, M.J.; Schulz, M.J.; “Sensory Systems for Assessing Damage Growth in Composite 

Materials,” Ninth International Conference on Composites Engineering, July 1−6, 2002, San Diego, 

CA. 

28. Elwasila, N.; Sundaresan, M.J.; Sankar, J.; Schulz, M.J.; “A Vibration Based Technique for 

Detecting Damage in Composite Bars,” Ninth International Conference on Composites Engineering, 

July 1−6, 2002, San Diego, CA. 

29. Gangadhararao, G.; Sankar, J.; Sundaresan, M.J.; Schulz, M.J. “Assessing Damage Growth in 

Composite Materials Using a Continuous Sensor,” Ninth International Conference on Composites 

Engineering, July 1−6, 2002, San Diego, CA. 

30. Ali, B.; Ferguson, F.; Sundaresan, M.J.; Schulz, M.J. “Monitoring the Onset of Column Instability 

Using Stress Wave Propagation,” Ninth International Conference on Composites Engineering, July 

1−6, 2002, San Diego, CA. 

31. Ferguson, F.; Sundaresan, M.J.; Grandhi, G.; Datta, S.; Kirikera, G.R.; Schulz, M.J. “Evaluation of a 

Neural System for Structural Health Monitoring,” First European Workshop on Structural Health 

Monitoring, Ecole Normale Supérieure, Cachan, Paris, July 10−12, 2002. 

32. Ghoshal, A.; Martin, W.N.; Schulz, M.J. “Simulation of Asymmetric Lamb Wave Propagation for 

Health Monitoring,” First European Workshop on Structural Health Monitoring, Ecole Normale 

Supérieure, Cachan, Paris, July 10−12, 2002. 

33. Datta, S.; Kirikera, G.R.; Schulz, M.J.; Sundaresan, M.J. “Sensor Design for an Artificial Neural 

System,” Ninth International Conference on Composites Engineering, July 1−6, 2002, San Diego, 

CA. 

34. Kirikera, G.R.; Datta, S.; Schulz, M.J.; Sundaresan, M.J. “An Artificial Neural System for Health 

Monitoring of Large Structures,” Ninth International Conference on Composites Engineering, July 

1−6, 2002, San Diego, CA. 

35. Schulz, M.J.; Kirikera, G.R.; Datta, S.; Sundaresan, M.J. “Piezoceramic and Nanotube Materials for 

Health Monitoring,” SPIE Conference, San Diego, CA, March 18, 2002. 

36. Ghoshal, A.; Martin, W.N.; Schulz M.J.; Chattopadhyay, A.; Prosser, W.H. “Wave Propagation 

Sensing for Damage Detection in Plates,” SPIE Conference, San Diego, CA, March 18, 2002. 

37. Martin, W.N.; Ghoshal, A.; Sundaresan, M.J.; Schulz, M.J.; Lebby, G. “Artificial Neural System for 

Structural Monitoring,” SPIE Conference, San Diego, CA, March 18, 2002. 

Book Chapter: 

1. Martin, W.N.; Ghoshal, A.; Sundaresan, M.J.; Schulz, M.J.; “Structural Health Monitoring Using an 

Artificial Neural System,” Recent Research Developments in Sound and Vibration, Transworld 

Research Network, Reference number TRN/SV/UA/S0008, 2003. 

 

SME Technical Paper: 

1. Datta, S.; Kirikera, G.R.; Schulz, M.J.; Sundaresan, M.J. “Sensor Design for an Artificial Neural 

System,” published by the Society of Manufacturing Engineers (SME), Dearborn, MI, as an SME 

Technical Paper, EE02-314, December 2002. This paper is disseminated in hard-copy form, on 

microfiche, and electronically through SME’s Web site. 

41
11.0 Undergraduate Student Design Project

Students in one section of the undergraduate Machine Analysis and Design course MECH 321, taught at 

the University of Cincinnati, participated in a wind turbine design project for the period 2002 to 2004. 

Mark Schulz teaches this course to all the UC Mechanical Engineering Students at the University of 

Cincinnati. Each year about 30 of the 75 pre-junior students form project groups of three students per 

group and each group designs the low speed shaft of a horizontal axis wind turbine. Mr. Alan Laxson of 

NREL was the technical monitor of this project and helped us obtain information for the design of the 

wind turbines by allowing us to get design information from Mr. David Malcolm. Mr. Malcolm provided 

much information and advice on wind turbine design. The shaft design is based on loading for a 1.5MW 

turbine. The design analysis used the Distortion Energy-Gerber method for predicting fatigue life under 

combined loading with fluctuating and mid-range stresses. The students came up with some unusual 

preliminary designs for wind turbine shafts. These included hollow shafts to minimize weight, hollow 

tapered shafts to minimize weight by maintaining a uniform stress level along the shaft, and shafts made 

of different metals and composite materials. The students also suggested that the blade and hub might be 

redesigned to move the blade closer to the bearing to reduce the bending stress due to the weight of the 

blades and hub. 

Contact information for Mr. Laxson and Mr. Malcolm follows. 

Alan Laxson 

National Renewable Energy Laboratory 

1617 Cole Blvd. 

Golden, CO 80401 

E-mail: alan_laxson@nrel.gov

David Malcolm 

Global Energy Concepts, LLC 

5729 Lakeview Drive NE, Suite 100 

Kirkland, Washington 98033-7340 

Phone: 425-822-9008 Ext 17 

Fax 425-822-9022 

E-mail: dmalcolm@globalenergyconcepts.com 

42
12.0 Acknowledgments 

The grant technical monitor for this project is Mr. Alan Laxson. His efforts in directing the research and 

providing contacts with other researchers at NREL, Sandia Labs, and in industry are a large factor in the 

success of the research. The undergraduate students also thank Mr. David Malcolm and Mr. Alan Laxson 

for providing information on wind turbines for the course design projects. Mr. Scott Hughes, V. 

Gevorgian, and J. VanDamof NREL participated in the wind turbine testing and made the testing 

successful. 

12.1 Faculty and Staff Working on the Project 

The faculty and staff that worked on the project are listed below. 

• Mannur Sundaresan, Associate Professor, North Carolina A&T State University 

• Douglas Hurd, Machinist, University of Cincinnati 

• Mark J. Schulz, Associate Professor; University of Cincinnati. 

 

12.2 Students Supported 

The students that were partially supported to work on the project are listed below. The students received 

support from other projects also. 

• Saurabh Datta, Goutham Kirikera, SriLaxmi Pammi, Inpil Kang, graduate students, University of 

Cincinnati 

• Jacob Hause (co-op student with outside leveraged support), undergraduate student, University of 

Cincinnati 

• Gangadhararao Grandhi, Bashir Ali, North Carolina A&T State University. 

43
F1146-E(12/2004) 

REPORT DOCUMENTATION PAGE Form Approved 

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PLEASE DO NOT RETURN YOUR FORM TO THE ABOVE ORGANIZATION.

1. REPORT DATE (DD-MM-YYYY)

August 2006 

2. REPORT TYPE

Subcontract report 

3. DATES COVERED (From - To)

May 30, 2002 - April 30, 2006 

5a. CONTRACT NUMBER

DE-AC36-99-GO10337 

5b. GRANT NUMBER

4. TITLE AND SUBTITLE

A Smart Sensor System for Structural Condition Monitoring of Wind 

Turbines 

5c. PROGRAM ELEMENT NUMBER

5d. PROJECT NUMBER

NREL/SR-500-40089 

5e. TASK NUMBER

WER6.0301 

6. AUTHOR(S)

M.J. Schulz and M.J. Sundaresan 

5f. WORK UNIT NUMBER

7. PERFORMING ORGANIZATION NAME(S) AND ADDRESS(ES)

M.J. Schulz: University of Cincinnati, Cincinnati, Ohio 

M.J. Sundaresan: North Carolina A&T State University, Greensboro, North 

Carolina 

8. PERFORMING ORGANIZATION 

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XCX-2-31214-01 

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NREL 

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National Renewable Energy Laboratory 

1617 Cole Blvd. 

Golden, CO 80401-3393 11. SPONSORING/MONITORING 

AGENCY REPORT NUMBER

NREL/SR-500-40089 

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13. SUPPLEMENTARY NOTES

NREL Technical Monitor: Alan Laxson 

14. ABSTRACT (Maximum 200 Words)

This report describes the efforts of the University of Cincinnati, North Carolina A&T State University, and NREL to 

develop a structural neural system for structrual health monitoring of wind turbine blades. 

15. SUBJECT TERMS

wind turbine blade test; structural test; structural health monitoring; structural neural system; wind energy; wind 

turbine 

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