Goutham R. Kirikera

Adaptive Fiber Bragg Grating Sensor Network for Structural Health Monitoring: Applications to Impact Monitoring:

A passive structural health monitoring (SHM) system for locating foreign-object impact using a network of fiber Bragg grating (FBG) sensors that monitor high frequency dynamic strains is described. The FBG sensor signals are adaptively demodulated using a two-wave mixing (TWM) spectral demodulator. Strains applied on the FBG sensors are encoded as wavelength shifts of the light reflected by the FBG sensor which are then converted into phase shifts and demodulated by the TWM interferometer. The demodulator adaptively compensates for low frequency drifts caused by large quasi-static strain and temperature drift and allows only high frequency signals to pass through. The FBG sensor network is mounted on a plate, and the structure is subjected to artificial impacts generated by dropping small ball bearings. Owing to the directional sensitivity of the FBG sensors, an FBG sensor-pair configuration is used at each sensing location. The impact signals from multiple FBG sensors are simultaneously acquired at frequencies of up to 180kHz. Using time-frequency wavelet analysis, the group velocity dispersion curve of the detected Lamb wave modes is obtained from the measured transient responses of the sensors, and this is used to determine the location of the impact.

A Structural Neural System for Real-time Health Monitoring of Composite Materials

A prototype structural neural system (SNS) is tested for the first time and damage detection results are presented in this study. The SNS is a passive online structural health monitoring (SHM) system that mimics the synaptic parallel computation networks present in the human biological neural system. Piezoelectric ceramic sensors and analog electronics are used to form neurons that measure strain waves generated by damage. The sensing of strain waves is similar to the proven nondestructive evaluation (NDE) technique of acoustic emission (AE) monitoring. Fatigue testing of a composite specimen on a four-point bending fiXture is performed, and the SNS is used to monitor the specimen for damage in real time. The prototype SNS used four sensors as inputs, but the number of inputs can be in the tens or hundreds depending on the type of SNS processor used. This is an area of continuing development. The SNS has two channels of signal output that are digitized and processed in a computer. The first output channel tracks the propagation of waves due to damage, and the second output channel provides the combined AE responses of the sensors. The data from these two channels are used to predict the location of damage and to qualitatively indicate the severity of the damage. Overall, this study shows that the SNS can detect damage growth in composites during operation of the structure, and the SNS architecture has the potential to tremendously simplify the AE technique for use in on-board SHM. Ten or more input neurons can be used, and still only two output channels are needed. Two levels of monitoring are possible using the SNS; a coarser SHM approach, or an on-board NDE approach. The SHM approach uses the SNS with a coarse grid of neurons to monitor and detect damage occurring in a general area during operation of the structure. The SNS will indicate where and when a more sensitive inspection is needed which can be done using ground-based NDE techniques. The on-board NDE approach uses the SNS with a fine coverage of neurons for highly sensitive NDE which continuously listens for damage and provides real-time processing and information about any damage in the structure and the performance limits and safety of the vehicle.

Structural health monitoring using continuous sensors and neural network analysis

A method for damage detection in a plate structure is presented based on strain waves that are generated by foreign object impact on the structure, or by damage that is propagating in the structure. The response characteristics of continuous sensors, which are long ribbon-like sensors, were studied by simulation of wave propagation in a panel. The advantage of the continuous sensor is to improve damage detection by having a large coverage of sensors on the structure using a small number of channels of data acquisition. Strain responses from the continuous sensors were used to estimate the damage location using a neural network technique. Eight hundred numerical wave propagation simulation runs for a plate were carried out to train the neural network and verify the proposed method for damage localization. The identified damage locations agreed reasonably well with the exact damage locations. Overall, the approach presented is meant to simplify the instrumentation needed for damage detection by using continuous sensors, a small number of channels of data acquisition, and training a neural network to do the work of locating the damage source.

Monitoring Multi-Site Damage Growth During Quasi-Static Testing of a Wind Turbine Blade using a Structural Neural System:

Structural Health Monitoring (SHM) of a wind turbine blade using a Structural Neural System (SNS) is described in this paper. Wind turbine blades are composite structures with complex geometry and sections that are built of different materials. The 3D structure, large size, anisotropic material properties, and the potential for damage to occur anywhere on the blade makes damage detection a significant challenge. A SNS based on acoustic emission (AE) monitoring (passive listening) was developed for practical low cost SHM of large composite structures such as wind turbine blades. The SNS was tested to detect damage initiation and propagation on a 9 m long wind turbine blade during a quasi-static proof test to failure at the National Renewable Energy Laboratory test facility in Golden, Colorado. Twelve piezoelectric sensors were bonded on the surface of the wind turbine blade and connected to form four continuous sensors which were used in the SNS to determine damage locations. Although 12 sensors monitored the wind turbine blade, the SNS produces only two analog output signals; one time signal to determine and locate damage, and a second time signal containing combined AE waveforms. Testing of the wind turbine blade produced some interesting results. After initial emissions due to settling of the blade diminished, damage initiated at one location on the blade. As the load was increased, damage occurred in a sequence at three other locations until there was a catastrophic buckling failure of the blade. The buckling occurred above the design load for the blade, and was due to the carbon spar cap disbonding from the fiberglass shear web under compressive bending stress. The SNS indicated the general area where the damage started and how the damage progressed, which is valuable information for verifying and improving the blade design and the manufacturing procedure. Strain gages on the blade did not provide a clear indication of damage until buckling occurred. A major outcome of this testing was to provide confidence that SHM of large composite structures that have complex geometry and multiple materials is practical using a simple, low cost SNS.

Initial evaluation of an active/passive structural neural system for health monitoring of composite materials

Structural health monitoring is an underlying technology that can help to ensure safe operation and provide cost effective maintenance of advanced composite structures. While several general methods of health monitoring have evolved in recent years, there is still the goal of reducing the overall cost of applying health monitoring to large structures. Data acquisition hardware typically consumes most of the investment in a structural monitoring system. On a conventional system based on acoustic emission monitoring, a separate high sampling rate data acquisition channel is needed for each sensor to convert analog signals to digital signals to locate damage. Other methods of damage detection are likewise complicated, and require many sensors and actuators, auxiliary signal processing, and data storage instrumentation. This paper proposes a structural neural system that uses firing of sensor neurons to reduce the number of data acquisition channels needed for damage detection. The neural system can perform passive acoustic emission sensing or active wave propagation monitoring. A prototype structural neural system with four sensor inputs was built and tested, and experimental results are presented in the paper. One signal output from the structural neural system is used to predict the location of damage. A second signal provides the time domain response of the sensors. Therefore, passive and active health monitoring can be performed using two channels of data acquisition. The structural neural system significantly reduces the data acquisition hardware required for health monitoring, and combines some of the advantages that exist individually for passive and active health monitoring.

Recent advances in an artificial neural system for structural health monitoring

This paper discusses recent advances in modeling and simulation of an artificial neural system and simulation of wave propagation for designing structural health monitoring systems. An artificial neural system was modeled using piezoceramic nerves and electronic components. Wave propagation in a panel is modeled using classical plate theory and a closed-form solution of wave propagation and reflection is obtained. Equations representing a half sine input similar to a projectile impact or a tone burst excitation were added to the existing algorithm that predicts the response of the artificial neural system due to impulse inputs. Firing switches have been modeled in the simulation to predict the sequential firing of the neurons as the waves pass over them. Also, new active fiber sensors have been designed for use in the artificial neural system. Simulated responses of the artificial neural system are shown in this paper and indicate that large neural systems can be formed with hundreds of sensor nodes. Experiments were performed to study a small neural system on a glass fiber panel. Waves were induced in the panel due to a lead break to simulate a crack and due to an impact from an impact hammer. Testing showed the location of a crack could be determined within the unit cell of the neural system for an orthotropic panel.

Continuous sensors for structural health monitoring

This paper discusses the development of continuous Active Fiber Composite sensors to detect damage in composite materials. Continuous sensors contain multiple interconnected sensor nodes that can be integrated into an artificial neural system as an array of sensor nerves. Continuous sensors have demonstrated a possibility of damage detection in large structures when used as a part of Artificial Neural System. The advantage of this passive health monitoring approach is that the sensor system is highly distributed and uses parallel processing allowing large structures to be monitored for damage using a small number of channels of data acquisition. In the paper, the continuous sensor system is modeled and simulated by solving the elastic response of a plate and the coupled piezoelectric constitutive equations. The model and simulation allow the sensor system to be optimized for a particular material and plate size. The simulation predicts that acoustic waves representative of damage growth can be detected anywhere in the plate using a simple artificial neural system. To improve the sensitivity of the continuous sensor, unidirectional active fiber composite sensors were built from piezoceramic ribbon preforms. Manufacturing of the active fiber composite sensors is discussed in the paper. The continuous sensors were evaluated in a realistic test to show their ability detect acoustic emissions caused by damage to a composite material. The sensors were mounted on narrow glass fiber plates and tested to failure in a mechanical test machine. Results from the experiments are also presented.

Mimicking the biological neural system using electronic logic circuits

Detecting and locating cracks in structural components and joints that have high feature densities is a challenging problem in the field of Structural Health Monitoring. There have been advances in piezoelectric sensors, actuators, wave propagation, MEMS, and optical fiber sensors. However, few sensor-signal processing techniques have been applied to the monitoring of joints and complex structural geometries. This is in part because maintaining and analyzing a large amount of data obtained from a large number of sensors that may be needed to monitor joints for cracks is difficult. Reliable low cost assessment of the health of structures is crucial to maintain operational availability and productivity, reduce maintenance cost, and prevent catastrophic failure of large structures such as wind turbines, aircraft, and civil infrastructure. Recently, there have also been advances in development of simple passive techniques for health monitoring including a technique based on mimicking the biological neural system using electronic logic circuits. This technique aids in reducing the required number of data acquisition channels by a factor of ten or more and is able to predict the location of a crack within a rectangular grid or within an arbitrarily arranged network of continuous sensors or neurons. The current paper shows results obtained by implementing this method on an aluminum plate and joint. The plates were tested using simulated acoustic emissions and also loading via an MTS machine. The testing indicates that the neural system can monitor complex joints and detect acoustic emissions due to propagating cracks. High sensitivity of the neural system is needed, and further sensor development and testing on different types of joints is required. Also indicated is that sensor geometry, sensor location, signal filtering, and logic parameters of the neural system will be specific to the particular type of joint (material, thickness, geometry) being monitored. Also, a novel piezoresistive carbon nanotube nerve crack sensor is presented that can become a neuron and respond to local crack growth.

Active fiber composite continuous sensors

This paper examines the use of continuous sensors to detect damage in composite materials. Continuous sensors contain multiple interconnected sensor nodes that can be integrated into an artificial neural system as an array of sensor nerves. The advantage of this passive health monitoring approach is that the sensor system is highly distributed and uses parallel processing allowing large structures to be monitored for damage using a small number of channels of data acquisition. In the paper, the continuous sensor system is modeled and simulated by solving the elastic response of a plate and the coupled piezoelectric constitutive equations. The model and simulation allow the sensor system to be optimized for a particular material and plate size. The simulation predicts that acoustic waves representative of damage growth can be detected anywhere in the plate using a simple artificial neural system. To improve the sensitivity of the continuous sensor, unidirectional active fiber composite sensors were built from piezoceramic ribbon preforms. Manufacturing of the active fiber composite sensors is also discussed in the paper. The continuous sensors were evaluated in a realistic test to show their ability detect acoustic emissions caused by damage to a composite material. The sensors were mounted on narrow glass fiber plates and tested to failure in a mechanical test machine. Results from the experiments are presented.

Multiple damage identification on a wind turbine blade using a structural neural system

A large number of sensors are required to perform real-time structural health monitoring (SHM) to detect acoustic emissions (AE) produced by damage growth on large complicated structures. This requires a large number of high sampling rate data acquisition channels to analyze high frequency signals. To overcome the cost and complexity of having such a large data acquisition system, a structural neural system (SNS) was developed. The SNS reduces the required number of data acquisition channels and predicts the location of damage within a sensor grid. The sensor grid uses interconnected sensor nodes to form continuous sensors. The combination of continuous sensors and the biomimetic parallel processing of the SNS tremendously reduce the complexity of SHM. A wave simulation algorithm (WSA) was developed to understand the flexural wave propagation in composite structures and to utilize the code for developing the SNS. Simulation of AE responses in a plate and comparison with experimental results are shown in the paper. The SNS was recently tested by a team of researchers from University of Cincinnati and North Carolina A&T State University during a quasi-static proof test of a 9 meter long wind turbine blade at the National Renewable Energy Laboratory (NREL) test facility in Golden, Colorado. Twelve piezoelectric sensor nodes were used to form four continuous sensors to monitor the condition of the blade during the test. The four continuous sensors are used as inputs to the SNS. There are only two analog output channels of the SNS, and these signals are digitized and analyzed in a computer to detect damage. In the test of the wind turbine blade, multiple damages were identified and later verified by sectioning of the blade. The results of damage identification using the SNS during this proof test will be shown in this paper. Overall, the SNS is very sensitive and can detect damage on complex structures with ribs, joints, and different materials, and the system relatively inexpensive and simple to implement on large structures.