Electrical power transmission companies have been inspecting underground power cables in a time consuming and destructive way. The current methodology used by Manitoba Hydro, is to remove the conductive material in the center of the cable, cutting the cable into wafers leaving behind the insulating polymer material known as XLPE, the area where many faults occur, and inspect the wafers manually with a microscope. The main goal of this work was to find a methodology to detect these cable faults in a non-destructive way so that the quality of the cable may be assessed, and its remaining lifetime be estimated and return it to use if possible. Two XLPE power cable samples were tested. Three small holes were drilled in one XLPE cable. A capacitive transducer with center frequency of 802.8 kHz was applied for transmitting receiving signal. For each sample, 48 scans were collected. Based on ultrasound images, we were able to detect these faults in this XLPE material from the peaks of the samples corresponding to the XLPE area by setting a threshold to 0.08 volts. Also, this detection technique was improved by using fourth-order cumulants. / May 2016

Accurate ship-motion prediction is important because it is directly related to the design, control, and economic operation of ships. Many methods are available for studying and predicting ship motions, including time-domain, strip-theory, and system-identification-based predictions. Time-domain and strip-theory predictions suffer from several physical and computational limitations. In this work, we use system-identification techniques to predict ship motions. We establish an identification methodology that can handle general finite-degree-of-freedom (FDOF) models of ship motions. To establish this methodology, we derive the correct form of the equations of motion. This form contains all relevant linear and nonlinear terms. Moreover, it explicitly specifies the dependence of the linear and nonlinear parameters on the forward speed. The energy-formulation approach is utilized to obtain full nonlinear ship-motion equations. The advantages of using this formulation are that self-sustained motions are not allowed and the dependence of the parameters on the forward speed is derived explicitly. The data required for the identification techniques are generated using the Large Amplitude Motions Program (LAMP) developed by the Science Applications International Corporation (SAIC). The ship studied in this work is a Series 60 ship, which is a military cargo ship. LAMP data for different sea states and forward speeds are used to identify and predict the ship motions. For linear parametric identification, we use the Eigensystem Realization Algorithm (ERA) to determine the coefficients in the linearly coupled equations and the effects of the forward speed on these coefficients. For linear nonparametric identification, we present a new analysis technique, namely, the circular-hyperbolic decomposition (CHD), which avoids the leakage effects associated with the discrete Fourier transform (DFT). The CHD is then utilized to determine transfer functions and response amplitude operators (RAOs). For nonlinear parametric identification, we present a methodology that is a combination of perturbation techniques and higher-order spectral moments. We apply this methodology to identify the nonlinear parameters that cause parametric roll resonance. The level of accuracy of the models and the parameter estimates are determined by validations of the predicted ship motions with the LAMP data. / Ph. D.

Complex nonlinear aspects of aeroelastic phenomena include unsteady nonlinear aerodynamic loads, structural nonlinearities, as well as nonlinear couplings between the flow and the structural response. Nonlinearities in aerodynamic loads originate from unsteady shocks and/or flow separation. Structural nonlinearities are geometric, or a result of free play. Nonlinear fluid structure couplings result from nonlinear resonance between the aerodynamic load and structural modes. Under different conditions, one or a combination of these aspects could yield flutter or Limit Cycle Oscillations (LCO). The overall goal of this work is to develop the capabilities to quantify the role that these different nonlinear mechanisms could play in observed flutter and LCO. The realization of such a goal would help in providing a benchmark for the detection of nonlinear aeroelastic instabilities and possibly effective means for obtaining improved performance and reduced uncertainties through operation beyond conventional boundaries that are based on linear analysis. Additionally, this effort will provide a benchmark for the validation of computational methodologies. In this thesis, wavelet-based higher order spectra are applied to identify different nonlinear aeroelastic phenomena as encountered in two experiments. First, the analysis is applied to a set of experiments involving a flexible semispan model (FSM) of a High Speed Civil Transport (HSCT) wing configuration conducted by Silva et al. (Experimental Steady and Unsteady Aerodynamic and Flutter Results for HSCT Semispan Models; AIAA/ASME/ASCE/AHS/ASC 41st Structures, Structural Dynamics, and Materials Conference, 2000). The interest is in the identification of nonlinear aeroelastic phenomena associated with a high dynamic response region which was measured over a large range of dynamic pressures around Mach number 0.98. At the top of this region is a ``hard'' flutter point that resulted in the loss of the model. The results show that ``hard'' flutter is related to intermittent nonlinear coupling between the shock motion and large amplitude structural motions. Second, the analysis is applied to identify nonlinear aspects of LCO encountered during test flights of an F-16 aircraft. The results show quadratic and cubic couplings in the acceleration signals of the under-wing launchers and high quadratic coupling levels between flaperon motions and wing oscillations. The implications of applying these techniques in the capacity of a ``flutterometer'' are also discussed. / Ph. D.

Virus recognition by visual examination of electron microscope (EM) images is time consuming and requires highly trained and experienced medical specialists. For these reasons, it is not suitable for screening large numbers of specimens. The objective of this research was to develop a reliable and robust pattern recognition system that could be trained to detect and classify different types of viruses from two-dimensional images obtained from an EM. This research evaluated the use of radial spectra of higher order spectral invariants to capture variations in textures and differences in symmetries of different types of viruses in EM images. The technique exploits invariant properties of the higher order spectral features, statistical techniques of feature averaging, and soft decision fusion in a unique manner applicable to the problem when a large number of particles were available for recognition, but were not easily registered on an individual basis due to the low signal to noise ratio. Experimental evaluations were carried out using EM images of viruses, and a high statistical reliability with low misclassification rates was obtained, showing that higher order spectral features are effective in classifying viruses from digitized electron micrographs. With the use of digital imaging in electron microscopes, this method can be fully automated.

No description available.

The understanding of how structural components endure loads, in particular variable loads, is that these components gradually, over some period of time depending on the nature of the loading and the material, develop a microcrack. After some additional time and loading, the microcrack grows to a size that might be detected. Beyond that point, the microcrack propagates in a manner that can be reliably predicted by computer analysis codes. Consequently, one can define different stages for the life of a structural component. These are: 1) the period prior to the formation of a microcrack, 2) the period of microcrack growth, and finally 3) the period of crack growth. To date, structural health monitoring approaches that seek to detect cracks offer no insight into the extent of deterioration occurring in the initial stage that is a precursor to the formation of the microcrack or its growth. However, an approach that would facilitate monitoring the extent of the deterioration that takes place during this stage promises to improve life prediction capabilities of structural components. The challenge, thus, is to develop quantitative assessment of damage accumulation from the earliest stages of the fatigue process and to provide a structure's signature that is dependent of the damage stage. One such signature is the structure's response to forced excitation. The realization of such a goal would help in advancing structural health monitoring procedures using interrogative system identification techniques and determine sensitivities of physical parameters to damage. Additionally, vibration-based spectral quantities are related to physical properties of the structure under test. In this thesis, nonlinear response to parametric excitation is exploited for nonlinear system identification of metallic and composite beam-mass systems before damage initiation through intermediate states of damage progression to failure. Parametric identification procedure combines linear and higher order spectral analysis of vibration measurements and perturbation techniques for the derivation of the approximate solution of the system nonlinear governing differential equation. The possibility of using optical Fiber Bragg Grating sensors technology for damage localization is also assessed. Spectral moments and quantities obtained from fiber optic strain measurements are evaluated near and away from cracks to assess the relation between these moments and cracks. Variations in parameters representing natural frequency, damping and effective nonlinearities for different levels of progressive damage in a beam-mass system have been determined. Their percentage variations have been quantified to establish their sensitivities to damage initiation. The results show that damping and effective nonlinearity parameters are more sensitive to damage conditions than the natural frequency of the first mode. Crack localization is assessed by means of optical fiber technology for a composite beam-mass system. The results show that noise levels in fiber optic signals are high in comparison to strain gage signals. Of particular interest, however, is the observation that the nonlinear response is more pronounced near the cracks than away from them. / Ph. D.

The full-scale value of the Reynolds number associated with wind loads on structures is of the order of 10^7. This is further complicated by the high levels of turbulence fluctuations associated with strong winds. On the other hand, numerical and wind tunnel simulations are usually carried out at smaller values of Re. Consequently, the validation of these simulations should only be based on physical phenomena derived with tools capable of their identification. In this work, two physical aspects related to extreme wind loads on low-rise structures are examined. The first includes the statistical properties and prediction of pressure peaks. The second involves the identification of linear and nonlinear relations between pressure peaks and associated velocity fluctuations. The first part of this thesis is concerned with the statistical properties of surface pressure time series and their variations under different incident flow conditions. Various statistical tools, including space-time correlation, conditional sampling, the probability plot and the probability plot correlation coefficient, are used to characterize pressure peaks measured on the top surface of a surface-mounted prism. The results show that the Gamma distribution provides generally the best statistical description for the pressure time series, and that the method of moments is sufficient for determining its parameters. Additionally, the shape parameter of the Gamma distribution can be directly related to the incident flow conditions. As for prediction of pressure peaks, the results show that the probability of non-exceedence can best be derived from the Gumbel distribution. Two approaches for peak prediction, based on analysis of the parent pressure time series and of observed peaks, are presented. The prediction based on the parent time series yields more conservative estimates of the probability of non-exceedence. The second part of this thesis is concerned with determining the linear and nonlinear relations between pressure peaks and the velocity field. Validated by analytical test signals, the wavelet-based analysis is proven to be effective and accurate in detecting intermittent linear and nonlinear relations between the pressure and velocity fluctuations. In particular, intermittent linear and nonlinear velocity pressure relations are observed over the nondimensional frequency range fH/U<0.32. These results provide the basis for flow parameters and characteristics required in the simulation of the wind loads on structures. / Ph. D.

One of the most significant goals in coal mining technology research is the automation of underground coal mining machinery. A current challenge with automating underground coal mining machinery is measuring and maintaining a coal mining horizon. The coal mining horizon is the horizontal path the machinery follows through the undulating coal seam during the mining operation. A typical mining practice is to leave a thin remnant of coal unmined in order to maintain geological stability of the cutting face. If the remnant layer is too thick, resources are wasted as the unmined coal is permanently unrecoverable. If the remnant layer is too thin, the product is diluted by mining into the overburden and there is an increased risk of premature roof fall which increases danger. The main challenge therefore is to develop a robust sensing method to estimate the thickness of thin remant coal layers. This dissertation addresses this challenge by presenting a pattern recognition methodology to estimate thin remnant coal layer thickness using ground penetrating radar (GPR). The approach is based upon a novel feature vector, derived from the bispectrum, that is used to characterise the early-time segment of 1D GPR data. The early-time segment is dominated by clutter inherent in GPR systems such as antenna crosstalk, ringdown and ground-bounce. It is common practice to either time-gate the signal, disregard the clutter by rendering the early-time segment unusable, or configure the GPR equipment to minimise the clutter effects which in turn reduces probing range. Disregarding the early-time signal essentially imposes a lower thickness limit on traditional GPR layer thickness estimators. The challenges of estimating thin layer thickness is primarily due to these inherent clutter components. Traditional processing strategies attempt to minimise the clutter using pre-processing techniques such as the subtraction of a calibration signal. The proposed method, however, treats the clutter as a deterministic but unknown signal with additive noise. Hence the proposed approach utilises the energy from the clutter and monitors change in media from subtle changes in the signal shape. Two complementary processing methods important to horizon sensing have been also proposed. These methods, near-surface interface detection and antenna height estimation, may be used as pre-validation tools to increase the robustness of the thickness estimation technique. The proposed methods have been tested with synthetic data and validated with real data obtained using a low power 1.4 GHz GPR system and a testbed with known conditions. With the given test system, it is shown that the proposed thin layer thickness estimator and near-surface interface detector outperform the traditional matched filter based processing methods for layers less than 5 cm in thickness. It is also shown that the proposed antenna height estimator outperforms the traditional height estimator for heights less than 7 cm. These new methods provide a means for reliably extending layer thickness estimation to the thin layer case where traditional approaches are known to fail.