The health monitoring of smart structures in civil engineering is becoming more and more important as in-situ structural monitoring would greatly reduce structure life-cycle costs and improve reliability. The distributed strain and temperature sensing is highly desired in large structures where strain and temperature at over thousand points need to be measured simultaneously. It is difficult to carry out this task using conventional electrical strain sensors. Fiber optic sensors provide an excellent opportunity to fulfill this need due to their capability to multiplex many sensors along a single fiber cable. Numerous research studies have been conducted in past decades to increase the number of sensors to be multiplexed in a distributed sensor network. This dissertation presents detailed research work on the analysis, design, fabrication, testing, and evaluation of an intrinsic Fabry-Perot fiber optic sensor for quasi-distributed strain and temperature measurements. The sensor is based on two ultra-short and broadband reflection fiber Bragg gratings. One distinct feature of this sensor is its ultra low optical insertion loss, which allows a significant increase in the sensor multiplexing capability. Using a simple integrated sensor interrogation unit and an optical spectrum based signal processing algorithm, many sensors can be interrogated along a single optical fiber with high accuracy, high resolution and large dynamic range. Based on the experimental results and theoretical analysis, it is expected that more than 500 sensors can be multiplexed with little crosstalk using a frequency-division multiplexing technology. With this research, it is possible to build an easy fabrication, robust, high sensitivity and quasi-distributed fiber optic sensor network that can be operated reliably even in harsh environments or extended structures. This research was supported in part by U.S. National Science Foundation under grant CMS-0427951. / Ph. D.

Structural Health Monitoring (SHM) is the implementation of damage detection and characterization algorithms using in vitro sensing and actuation for rapidly determining faults in structural systems before the damage leads to catastrophic failure. SHM systems provide near real time information on the state of the integrity of civil, mechanical and aerospace structures. A roadblock in implementing SHM systems in practice is the possibility of false positives introduced by environmental changes. In particular, temperature changes can cause many SHM algorithms to indicate damage when no damage exists. While several experimentally based efforts have been attempted to alleviate temperature effects on SHM algorithms, fundamental research on the effects of temperature on SHM has not been investigated. The work presented in this dissertation composes of two main parts: the first part focuses on the experimental studies of different mechanical structures of aluminum beams, lug samples and railroad switch bolts. The experimental study of the aluminum lug samples and beams is done to propose and examine methods and models for in situ interrogation and detection of damage (in the form of a fatigue crack) in these specimen and to quantify the smallest detectable crack size in aluminum structures. This is done by applying the electrical impedance-based SHM method and using piezoceramic sensors and actuators. Moreover, in order to better extract the damage features from the measured electrical impedance, the ARX non-linear feature extraction is employed. This non-linear feature extraction, compared to the linear one, results in detection of damages in the micro-level size and improves the early detection of fatigue cracks in structures. Experimental results also show that the temperature variation is an important factor in the structural health monitoring applications and its effect on the impedance-based monitoring of the initiation and growth of fatigue cracks in the lug samples is experimentally investigated. The electrical impedance-based SHM technique is also applied in monitoring the loosening of bolted joints in a full-scale railroad switch and the sensitivity of this technique to different levels of loosening of the bolts is investigated. The second part of the work presented here focuses on the analytical study and better understanding of the effect of temperature on the vibration-based SHM. This is done by analytical modeling of the vibratory response of an Euler-Bernoulli beam with two different support conditions of simply supported and clamped-clamped and with a single, non-breathing fatigue crack at different locations along the length of the beam. The effect of temperature variations on the vibratory response of the beam structure is modeled by considering the two effects of temperature-dependent material properties and thermal stress formations inside the structure. The inclusion of thermal effects from both of these points of view (i.e. material properties variations and generation of thermal stresses) as independent factors is investigated and justified by studying the formulations of Helmholtz free energy and stresses inside a body. The effect of temperature variations on the vibratory response of the cracked beam are then studied by integrating these two temperature-related effects into the analytical modeling. The effect of a growing fatigue crack as well as temperature variations and thermal loadings is then numerically studied on the deflection of the beam and the output voltage of a surface-bonded piezoceramic sensor. / Ph. D.

Damage detection in mechanical and aerospace structures is critical to maintaining safe and optimal performance. The early detection of damage increases safety and reduces cost of maintenance and repair. Structural Health Monitoring (SHM) integrates sensor networks and structures to autonomously interrogate the structure and detect damage. The development of robust SHM systems is becoming more vital as aerospace structures are becoming more complex. New SHM methods that can determine the health of the structure without using traditional non-destructive evaluation techniques will decrease the cost and time associated with these investigations. The primary SHM method uses the signals recorded on a pristine structure as a reference and compares operational signals to the baseline measurement. One of the current limitations of baseline SHM is that environmental factors, such as temperature and stress, can change the system response so the algorithm indicates damage when there is none. Many structures which can benefit from SHM have multiple components and often have connections and interfaces that also can change under environmental conditions, thus changing the dynamics of the system. This dissertation addresses some of the current limitations of SHM. First the changes that temperature variations and applied stress create on Lamb wave propagation velocity in plates is analytically modeled and validated. Two methods are developed for the analytical derivative of the Lamb wave velocity; the first uses assumes a thermoelastic material while the second expands thermoelastic theory to include thermal expansion and the associated stresses. A model is developed so the baseline measurement can be compensated to eliminate the false positives due to environmental conditions without storage of dispersion curves or baseline signals at each environmental state. Next, a wave based instantaneous baseline method is presented which uses the comparison of simultaneously captured real time signals and can be used to eliminate the influence of environmental effects on damage detection. Finally, wave transmission and conversion across interfaces in prestressed bars is modeled to provide a better understanding of how the coupled axial and flexural dynamics of a non-ideal preloaded interface change with applied load. / Ph. D.

Structural health monitoring (SHM) is an anomaly detection process. Data-driven SHM has gained much attention compared to the model-based strategy, specifically with the current state-of-the-art machine learning routines. Model-based methods require structural information, time-consuming model updating, and may fail with noisy data, a persistent condition in real-time SHM problems. However, there are several hindrances in supervised and unsupervised settings in machine learning-based SHM. This study identifies and addresses such hindrances with the versatility of state-of-the-art deep learning strategies. While managing those complications, we aim at proposing a general, structure-independent (ie requires no prior information) SHM framework. Developing such techniques plays a crucial role in the SHM of smart cities. In the supervised SHM and sensor output validation (SOV) category, data class imbalance results from the lack of data from nuanced structural states. Employing Long Short-Term Memory (LSTM) units, we developed a general technique that manages both SHM and SOV. The developed architecture accepts high-dimensional features, enabling the train of Generative Adversarial Networks for data generation, addressing the complications of data imbalance. GAN-generated SHM data improved accuracy for low-sampled classes from 44.77% to 64.58% and from 73.39% to 90.84% in two SOV and SHM case studies, respectively. Arguing the unsupervised SHM as a practical category since it identifies novelties (ie unseen states), the current application of dimensionality reduction (DR) in unsupervised SHM is investigated. Due to the curse of dimensionality, classical unsupervised techniques cannot function with high-dimensional features, driving the use of DR techniques. Investigations highlighted the importance of avoiding DR in unsupervised SHM, as data dimensions that DR suppresses may contain damage-sensitive features for novelties. With DR, novelty detection accuracy declined up to 60% in two benchmark SHM datasets. Other obstacles in the unsupervised SHM area are case-dependent features, lack of dynamic-class novelty detection, and the impact of user-defined detection parameters on novelty detection accuracy. We chose the fast Fourier transform-based (FFT) of raw signals with no dimensionality reduction to develop the SHM framework. A deep neural network scheme is developed to perform the pattern recognition of that high-dimensional data. The framework does not require prior information, with GAN models implemented, offering robustness to sensor placement in structures. These characteristics make the framework suitable for developing general unsupervised SHM techniques. / Doctor of Philosophy / Detecting abnormal behaviors in structures from the input signals of sensors is called Structural health monitoring (SHM). The vibrational characteristics of signals or direct pattern recognition techniques can be applied to detect anomalies in a data-driven scheme. Machine learning (ML) tools are suitable for data-driven methods; However, challenges exist on both supervised and unsupervised ML-based SHM. Recent improvements in deep learning are employed in this study to address such obstacles after their identification. In supervised learning, the data points for the normal state of structures are abundant, and datasets are usually imbalanced, which is the same issue for the sensor output validation (SOV). SOV must be present before SHM takes place to remove anomalous sensor outputs. First, a unified decision-making system for SHM and SOV problems is proposed, and then data imbalance is alleviated by generating new data objects from low-sampled classes. The proposed unified method is based on the recurrent neural networks, and the generation mechanism is Generative Adversarial Network (GAN). Results indicate improvements in accuracy metrics for data classes in the minority. For the unsupervised SHM, four major issues are identified, including data loss during feature extraction, case-dependency of such extraction schemes. These two issues are solved with the fast Fourier transform (FFT) of signals to be the features with no reduction in their dimensionality. The other obstacles are the lack of discrimination between different novel classes (ie only two classes of damage and undamaged) and the effect of the detection parameters, defined by users, on the SHM analysis. The latter two predicaments are also addressed by online generating new data objects from the incoming signal stream with GAN and tuning the detection system to have the same performance regarding user-defined parameters regarding GAN-generated data. The proposed unsupervised technique is further improved to be insensitive to the sensor placement on structures by employing recurrent neural network layers in the GAN architecture, with the GAN that has overfitted discriminator.

Maintenance of the structural integrity and damage detection are critical for all massive and complicated new and aging structures. A structural health monitoring (SHM) system intends to identify damage on the structure under monitoring, so that necessary action can be taken in advance to avoid catastrophic results. Impedance-based SHM utilizes a piezoelectric ceramic as a collocated actuator and sensor, which measures the electrical impedance of the piezoelectric ceramic over a certain frequency range. The impedance profile of a structure under monitoring is compared against a reference profile obtained from the healthy structure. An existing approach called the sinc method adopts a sinc wave excitation and performs traditional discrete Fourier transform (DFT) based structural condition assessment. The sinc method requires rather intensive computing and a digital-to-analog converter (DAC) to generate a sinc excitation signal. It also needs an analog-to-digital converter (ADC) to measure the response voltage, from which impedance profile is obtained through a DFT. This dissertation investigates system design approaches for impedance-based structural health monitoring (SHM), in which a primary goal is low power dissipation. First, we investigated behaviors of piezoelectric ceramics and proposed an electrical model in order to enable us to conduct system level analysis and evaluation of an SHM system. Unloaded and loaded piezoelectric ceramics were electrically modeled with lumped linear circuit components, which allowed us to perform system level simulations for various environmental conditions. Next, we explored a signaling method called the wideband method, which uses a pseudorandom noise (PN) sequence for excitation of the structure rather than a signal with a particular waveform. The wideband method simplifies generation of the excitation signal and eliminates a digital-to-analog converter (DAC). The system form factor and power dissipation is decreased compared to the previously existing system based on a sinc signal. A prototype system was implemented on a digital signal processor (DSP) board to validate its approach. Third, we studied another low-power design approach which employs binary signals for structural excitation and structural response measurement was proposed. The binary method measures only the polarity of a response signal to acquire the admittance phase, and compares the measured phase against that of a healthy structure. The binary method eliminates the need for a DAC and an ADC. Two prototypes were developed: one with a DSP board and the other with a microcontroller board. Both prototypes demonstrated reduction of power dissipation compared with those for the sinc method and for the wideband method. The microcontroller based prototype achieved an on-board SHM system. Finally, we proposed an analytical method to assess the quality of the damage detection for the binary method. Using our method, one can obtain the confidence level of a damage detection for a given damage distance. / Ph. D.

Structural health monitoring (SHM) has become a strong national interest because of the need of reliable and accurate damage detection methods for aerospace, civil and mechanical engineering infrastructure. Health monitoring of these structures usually requires the sensors to have such features as large area coverage, maintenance free or minimum maintenance, ultra-low cost per measurement point, and capability of operation in harsh environments. Fiber Bragg grating (FBG) has attracted considerable interest for this application because of its compactness, electromagnetic immunity, and excellent multiplexing capability. Several FBG multiplexing techniques have been developed to increase the multiplexing number and further reduce the unit cost. To the author's best knowledge, the current demonstrated maximum multiplexing number are 800 FBG sensors in a single array using optical frequency domain reflectometry (OFDR), whose maximum fiber span is limited by the coherence length of light source. In this work, we proposed and demonstrated a wavelength-scanning time-division multiplexing (WSTDM) of 1000 ultra-weak FBGs for distributed temperature sensing. In comparison with the OFDR method, the WSTDM method distinguishes the sensors by different time delays, and its maximum operation distance, which is limited by the transmission loss of the fiber, can be as high as tens of kilometers. The strong multiplexing capability and low crosstalk of the ultra-weak FBG sensors was investigated through both theoretical analysis and experiment. An automated FBG fabrication system was developed for fast FBG fabrication. With this WSTDM method, we multiplexed 1000 ultra-weak FBGs for distributed temperature sensing. Besides the demonstrated temperature measurement, the reported method can also be applied to measure other parameters, such as strain, pressure. / Ph. D.

The research presented in this thesis is all based on the impedance method for structural health monitoring. The impedance method is an electro-mechanical technique which utilizes a single piezoelectric transducer as both a sensor and actuator. Due to the high frequencies of excitation used for the method, the sensing area for damage detection can be very localized. Previous work has shown that wave propagation can be added to systems already equipped with hardware for impedance-based structural health monitoring. The work in this thesis shows what happens under varying temperature conditions for a structure being monitored with wave propagation. A technique to compensate for temperature fluctuations is also presented. The work presented here is an initial study to directly correlate the actual amount of damage in a composite specimen with a damage metric indicated by impedance-based structural health monitoring. Two different damage mechanisms are examined: transverse matrix cracking and edge delamination. With both composite defects, a sample is interrogated with the impedance method before and after damage is introduced. The exact amount of damage in each specimen is found using radiography and compared with the health monitoring results. Traditional impedance techniques require the use of a bulky and expensive impedance analyzer. With the trend of structural health monitoring moving towards unobtrusive sensors which can be permanently placed on a structure, an impedance analyzer does not lend itself to these small, low power consuming requirements. In this thesis, an initial attempt to miniaturize the hardware is described. A prototype impedance-based structural health monitoring system, incorporating wireless based communications, is fabricated and validated with experimental testing on a number of different structures. The first steps towards a complete self-contained, robust structural health monitoring sensor are presented. / Master of Science

This thesis describes the test rig and results from a project expanding the field of rotor health monitoring by using Active Magnetic Bearings (AMBs) as actuators for applying a variety of known force inputs to a spinning. Similar to modal analysis and other nondestructive evaluation (NDE) techniques which apply input signals to static structures in order to monitor responses; this approach allows for the measurement of both input and output response in a rotating system for evaluation. However, unlike these techniques, the new procedure allows for multiple forms of force input signals to be applied to a rotating structure. This technique is used on a rotating shaft supported in conventional bearings with an AMB actuator added to the system. This paper presents the results from this project including shaft rub and notch. An EDM notch was also tested to attempt a breathing scenario similar to breathing cracks. / Master of Science

Structural Health Monitoring (SHM) is the science and technology of monitoring and can assess the condition of aerospace, civil, and mechanical infrastructures using a sensing system integrated into the structure. SHM is capable of detecting, locating, and quantifying various types of damage such as cracks, holes, corrosion, delamination, and loose joints, and can be applied to various kinds of infrastructures such as buildings, railroads, windmills, bridges, and aircraft. A major technical challenge for existing SHM systems is high power consumption, which severely limits the range of its applications. In this thesis, we investigated adoption of acoustic emission detection to reduce power dissipation of SHM systems employing the impedance and the Lamb wave methods. An acoustic emission sensor of the proposed system continuously monitors acoustic events, while the SHM system is in sleep mode. The SHM system is evoked to perform the SHM operation only when there is an acoustic event detected by the acoustic emission sensor. The proposed system avoids unnecessary operation of SHM operations, which saves power, and the system is effective for certain applications such as spacecraft and wind turbine blades. We developed prototype systems using a Texas Instruments TMS320F2812 DSP evaluation board for the Lamb wave method and an MSP430 evaluation board for the impedance method. / Master of Science

The primary focus of this research is evaluation of feasibility, applicability, and accuracy of Doppler Light Detection And Ranging (LIDAR) sensors as non-contact means for measuring track speed, distance traveled, and curvature. Speed histories, currently measured with a rotary, wheel-mounted encoder, serve a number of useful purposes, one significant use involving derailment investigations. Distance calculation provides a spatial reference system for operators to locate track sections of interest. Railroad curves, using an IMU to measure curvature, are monitored to maintain track infrastructure within regulations. Speed measured with high accuracy leads to high-fidelity distance and curvature data through utilization of processor clock rate and left-and right-rail speed differentials during curve navigation, respectively. Wheel-mounted encoders, or tachometers, provide a relatively low-resolution speed profile, exhibit increased noise with increasing speed, and are subject to the inertial behavior of the rail car which affects output data. The IMU used to measure curvature is dependent on acceleration and yaw rate sensitivity and experiences difficulty in low-speed conditions. Preliminary system tests onboard a 'Hy-Rail' utility vehicle capable of traveling on rail show speed capture is possible using the rails as the reference moving target and furthermore, obtaining speed profiles from both rails allows for the calculation of speed differentials in curves to estimate degrees curvature. Ground truth distance calibration and curve measurement were also carried out. Distance calibration involved placement of spatial landmarks detected by a sensor to synchronize distance measurements as a pre-processing procedure. Curvature ground truth measurements provided a reference system to confirm measurement results and observe alignment variation throughout a curve. Primary testing occurred onboard a track geometry rail car, measuring rail speed over substantial mileage in various weather conditions, providing high-accuracy data to further calculate distance and curvature along the test routes. Tests results indicate the LIDAR system measures speed at higher accuracy than the encoder, absent of noise influenced by increasing speed. Distance calculation is also high in accuracy, results showing high correlation with encoder and ground truth data. Finally, curvature calculation using speed data is shown to have good correlation with IMU measurements and a resolution capable of revealing localized track alignments. Further investigations involve a curve measurement algorithm and speed calibration method independent from external reference systems, namely encoder and ground truth data. The speed calibration results show a high correlation with speed data from the track geometry vehicle. It is recommended that the study be extended to provide assessment of the LIDAR's sensitivity to car body motion in order to better isolate the embedded behavior in the speed and curvature profiles. Furthermore, in the interest of progressing the system toward a commercially viable unit, methods for self-calibration and pre-processing to allow for fully independent operation is highly encouraged. / Ph. D.