Matching Items (172)
Description
Current economic conditions necessitate the extension of service lives for a variety of aerospace systems. As a result, there is an increased need for structural health management (SHM) systems to increase safety, extend life, reduce maintenance costs, and minimize downtime, lowering life cycle costs for these aging systems. The implementation of such a system requires a collaborative research effort in a variety of areas such as novel sensing techniques, robust algorithms for damage interrogation, high fidelity probabilistic progressive damage models, and hybrid residual life estimation models. This dissertation focuses on the sensing and damage estimation aspects of this multidisciplinary topic for application in metallic and composite material systems. The primary means of interrogating a structure in this work is through the use of Lamb wave propagation which works well for the thin structures used in aerospace applications. Piezoelectric transducers (PZTs) were selected for this application since they can be used as both sensors and actuators of guided waves. Placement of these transducers is an important issue in wave based approaches as Lamb waves are sensitive to changes in material properties, geometry, and boundary conditions which may obscure the presence of damage if they are not taken into account during sensor placement. The placement scheme proposed in this dissertation arranges piezoelectric transducers in a pitch-catch mode so the entire structure can be covered using a minimum number of sensors. The stress distribution of the structure is also considered so PZTs are placed in regions where they do not fail before the host structure. In order to process the data from these transducers, advanced signal processing techniques are employed to detect the presence of damage in complex structures. To provide a better estimate of the damage for accurate life estimation, machine learning techniques are used to classify the type of damage in the structure. A data structure analysis approach is used to reduce the amount of data collected and increase computational efficiency. In the case of low velocity impact damage, fiber Bragg grating (FBG) sensors were used with a nonlinear regression tool to reconstruct the loading at the impact site.
ContributorsCoelho, Clyde (Author) / Chattopadhyay, Aditi (Thesis advisor) / Dai, Lenore (Committee member) / Wu, Tong (Committee member) / Das, Santanu (Committee member) / Rajadas, John (Committee member) / Papandreou-Suppappola, Antonia (Committee member) / Arizona State University (Publisher)
Created2011
Description
Many products undergo several stages of testing ranging from tests on individual components to end-item tests. Additionally, these products may be further "tested" via customer or field use. The later failure of a delivered product may in some cases be due to circumstances that have no correlation with the product's inherent quality. However, at times, there may be cues in the upstream test data that, if detected, could serve to predict the likelihood of downstream failure or performance degradation induced by product use or environmental stresses. This study explores the use of downstream factory test data or product field reliability data to infer data mining or pattern recognition criteria onto manufacturing process or upstream test data by means of support vector machines (SVM) in order to provide reliability prediction models. In concert with a risk/benefit analysis, these models can be utilized to drive improvement of the product or, at least, via screening to improve the reliability of the product delivered to the customer. Such models can be used to aid in reliability risk assessment based on detectable correlations between the product test performance and the sources of supply, test stands, or other factors related to product manufacture. As an enhancement to the usefulness of the SVM or hyperplane classifier within this context, L-moments and the Western Electric Company (WECO) Rules are used to augment or replace the native process or test data used as inputs to the classifier. As part of this research, a generalizable binary classification methodology was developed that can be used to design and implement predictors of end-item field failure or downstream product performance based on upstream test data that may be composed of single-parameter, time-series, or multivariate real-valued data. Additionally, the methodology provides input parameter weighting factors that have proved useful in failure analysis and root cause investigations as indicators of which of several upstream product parameters have the greater influence on the downstream failure outcomes.
ContributorsMosley, James (Author) / Morrell, Darryl (Committee member) / Cochran, Douglas (Committee member) / Papandreou-Suppappola, Antonia (Committee member) / Roberts, Chell (Committee member) / Spanias, Andreas (Committee member) / Arizona State University (Publisher)
Created2011
Description
Infant mortality rate of field deployed photovoltaic (PV) modules may be expected to be higher than that estimated by standard qualification tests. The reason for increased failure rates may be attributed to the high system voltages. High voltages (HV) in grid connected modules induce additional stress factors that cause new degradation mechanisms. These new degradation mechanisms are not recognized by qualification stress tests. To study and model the effect of high system voltages, recently, potential induced degradation (PID) test method has been introduced. Using PID studies, it has been reported that high voltage failure rates are essentially due to increased leakage currents from active semiconducting layer to the grounded module frame, through encapsulant and/or glass. This project involved designing and commissioning of a new PID test bed at Photovoltaic Reliability Laboratory (PRL) of Arizona State University (ASU) to study the mechanisms of HV induced degradation. In this study, PID stress tests have been performed on accelerated stress modules, in addition to fresh modules of crystalline silicon technology. Accelerated stressing includes thermal cycling (TC200 cycles) and damp heat (1000 hours) tests as per IEC 61215. Failure rates in field deployed modules that are exposed to long term weather conditions are better simulated by conducting HV tests on prior accelerated stress tested modules. The PID testing was performed in 3 phases on a set of 5 mono crystalline silicon modules. In Phase-I of PID test, a positive bias of +600 V was applied, between shorted leads and frame of each module, on 3 modules with conducting carbon coating on glass superstrate. The 3 module set was comprised of: 1 fresh control, TC200 and DH1000. The PID test was conducted in an environmental chamber by stressing the modules at 85°C, for 35 hours with an intermittent evaluation for Arrhenius effects. In the Phase-II, a negative bias of -600 V was applied on a set of 3 modules in the chamber as defined above. The 3 module set in phase-II was comprised of: control module from phase-I, TC200 and DH1000. In the Phase-III, the same set of 3 modules which were used in the phase-II again subjected to +600 V bias to observe the recovery of lost power during the Phase-II. Electrical performance, infrared (IR) and electroluminescence (EL) were done prior and post PID testing. It was observed that high voltage positive bias in the first phase resulted in little
o power loss, high voltage negative bias in the second phase caused significant power loss and the high voltage positive bias in the third phase resulted in major recovery of lost power.
o power loss, high voltage negative bias in the second phase caused significant power loss and the high voltage positive bias in the third phase resulted in major recovery of lost power.
ContributorsGoranti, Sandhya (Author) / Tamizhmani, Govindasamy (Thesis advisor) / Rogers, Bradley (Committee member) / Macia, Narciso (Committee member) / Arizona State University (Publisher)
Created2011
Description
Advancements in computer vision and machine learning have added a new dimension to remote sensing applications with the aid of imagery analysis techniques. Applications such as autonomous navigation and terrain classification which make use of image classification techniques are challenging problems and research is still being carried out to find better solutions. In this thesis, a novel method is proposed which uses image registration techniques to provide better image classification. This method reduces the error rate of classification by performing image registration of the images with the previously obtained images before performing classification. The motivation behind this is the fact that images that are obtained in the same region which need to be classified will not differ significantly in characteristics. Hence, registration will provide an image that matches closer to the previously obtained image, thus providing better classification. To illustrate that the proposed method works, naïve Bayes and iterative closest point (ICP) algorithms are used for the image classification and registration stages respectively. This implementation was tested extensively in simulation using synthetic images and using a real life data set called the Defense Advanced Research Project Agency (DARPA) Learning Applied to Ground Robots (LAGR) dataset. The results show that the ICP algorithm does help in better classification with Naïve Bayes by reducing the error rate by an average of about 10% in the synthetic data and by about 7% on the actual datasets used.
ContributorsMuralidhar, Ashwini (Author) / Saripalli, Srikanth (Thesis advisor) / Papandreou-Suppappola, Antonia (Committee member) / Turaga, Pavan (Committee member) / Arizona State University (Publisher)
Created2011
Description
Underwater acoustic communications face significant challenges unprecedented in radio terrestrial communications including long multipath delay spreads, strong Doppler effects, and stringent bandwidth requirements. Recently, multi-carrier communications based on orthogonal frequency division multiplexing (OFDM) have seen significant growth in underwater acoustic (UWA) communications, thanks to their well well-known robustness against severely time-dispersive channels. However, the performance of OFDM systems over UWA channels significantly deteriorates due to severe intercarrier interference (ICI) resulting from rapid time variations of the channel. With the motivation of developing enabling techniques for OFDM over UWA channels, the major contributions of this thesis include (1) two effective frequencydomain equalizers that provide general means to counteract the ICI; (2) a family of multiple-resampling receiver designs dealing with distortions caused by user and/or path specific Doppler scaling effects; (3) proposal of using orthogonal frequency division multiple access (OFDMA) as an effective multiple access scheme for UWA communications; (4) the capacity evaluation for single-resampling versus multiple-resampling receiver designs. All of the proposed receiver designs have been verified both through simulations and emulations based on data collected in real-life UWA communications experiments. Particularly, the frequency domain equalizers are shown to be effective with significantly reduced pilot overhead and offer robustness against Doppler and timing estimation errors. The multiple-resampling designs, where each branch is tasked with the Doppler distortion of different paths and/or users, overcome the disadvantages of the commonly-used single-resampling receivers and yield significant performance gains. Multiple-resampling receivers are also demonstrated to be necessary for UWA OFDMA systems. The unique design effectively mitigates interuser interference (IUI), opening up the possibility to exploit advanced user subcarrier assignment schemes. Finally, the benefits of the multiple-resampling receivers are further demonstrated through channel capacity evaluation results.
ContributorsTu, Kai (Author) / Duman, Tolga M. (Thesis advisor) / Zhang, Junshan (Committee member) / Tepedelenlioğlu, Cihan (Committee member) / Papandreou-Suppappola, Antonia (Committee member) / Arizona State University (Publisher)
Created2011
Description
In nearly all commercially successful internal combustion engine applications, the slider crank mechanism is used to convert the reciprocating motion of the piston into rotary motion. The hypocycloid mechanism, wherein the crankshaft is replaced with a novel gearing arrangement, is a viable alternative to the slider crank mechanism. The geared hypocycloid mechanism allows for linear motion of the connecting rod and provides a method for perfect balance with any number of cylinders including single cylinder applications. A variety of hypocycloid engine designs and research efforts have been undertaken and produced successful running prototypes. Wiseman Technologies, Inc provided one of these prototypes to this research effort. This two-cycle 30cc half crank hypocycloid engine has shown promise in several performance categories including balance and efficiency. To further investigate its potential a more thorough and scientific analysis was necessary and completed in this research effort. The major objective of the research effort was to critically evaluate and optimize the Wiseman prototype for maximum performance in balance, efficiency, and power output. A nearly identical slider crank engine was used extensively to establish baseline performance data and make comparisons. Specialized equipment and methods were designed and built to collect experimental data on both engines. Simulation and mathematical models validated by experimental data collection were used to better quantify performance improvements. Modifications to the Wiseman prototype engine improved balance by 20 to 50% (depending on direction) and increased peak power output by 24%.
ContributorsConner, Thomas (Author) / Redkar, Sangram (Thesis advisor) / Rogers, Bradley (Committee member) / Georgeou, Trian (Committee member) / Arizona State University (Publisher)
Created2011
Description
Building Applied Photovoltaics (BAPV) form an essential part of today's solar economy. This thesis is an effort to compare and understand the effect of fan cooling on the temperature of rooftop photovoltaic (PV) modules by comparing two side-by-side arrays (test array and control array) under identical ambient conditions of irradiance, air temperature, wind speed and wind direction. The lower operating temperature of PV modules due to fan operation mitigates array non uniformity and improves on performance. A crystalline silicon (c-Si) PV module has a light to electrical conversion efficiency of 14-20%. So on a cool sunny day with incident solar irradiance of 1000 W/m2, a PV module with 15% efficiency, will produce about only 150 watts. The rest of the energy is primarily lost in the form of heat. Heat extraction methods for BAPV systems may become increasingly higher in demand as the hot stagnant air underneath the array can be extracted to improve the array efficiency and the extracted low-temperature heat can also be used for residential space heating and water heating. Poly c-Si modules experience a negative temperature coefficient of power at about -0.5% /o C. A typical poly c-Si module would experience power loss due to elevation in temperature, which may be in the range of 25 to 30% for desert conditions such as that of Mesa, Arizona. This thesis investigates the effect of fan cooling on the previously developed thermal models at Arizona State University and on the performance of PV modules/arrays. Ambient conditions are continuously monitored and collected to calculate module temperature using the thermal model and to compare with actually measured temperature of individual modules. Including baseline analysis, the thesis has also looked into the effect of fan on the test array in three stages of 14 continuous days each. Multiple Thermal models are developed in order to identify the effect of fan cooling on performance and temperature uniformity. Although the fan did not prove to have much significant cooling effect on the system, but when combined with wind blocks it helped improve the thermal mismatch both under low and high wind speed conditions.
ContributorsChatterjee, Saurabh (Author) / Tamizhmani, Govindasamy (Thesis advisor) / Rogers, Bradley (Committee member) / Macia, Narciso (Committee member) / Arizona State University (Publisher)
Created2011
Description
Photovoltaic (PV) modules undergo performance degradation depending on climatic conditions, applications, and system configurations. The performance degradation prediction of PV modules is primarily based on Accelerated Life Testing (ALT) procedures. In order to further strengthen the ALT process, additional investigation of the power degradation of field aged PV modules in various configurations is required. A detailed investigation of 1,900 field aged (12-18 years) PV modules deployed in a power plant application was conducted for this study. Analysis was based on the current-voltage (I-V) measurement of all the 1,900 modules individually. I-V curve data of individual modules formed the basis for calculating the performance degradation of the modules. The percentage performance degradation and rates of degradation were compared to an earlier study done at the same plant. The current research was primarily focused on identifying the extent of potential induced degradation (PID) of individual modules with reference to the negative ground potential. To investigate this, the arrangement and connection of the individual modules/strings was examined in detail. The study also examined the extent of underperformance of every series string due to performance mismatch of individual modules in that string. The power loss due to individual module degradation and module mismatch at string level was then compared to the rated value.
ContributorsJaspreet Singh (Author) / Tamizhmani, Govindasamy (Thesis advisor) / Srinivasan, Devarajan (Committee member) / Rogers, Bradley (Committee member) / Arizona State University (Publisher)
Created2011
Description
Following the success in incorporating perceptual models in audio coding algorithms, their application in other speech/audio processing systems is expanding. In general, all perceptual speech/audio processing algorithms involve minimization of an objective function that directly/indirectly incorporates properties of human perception. This dissertation primarily investigates the problems associated with directly embedding an auditory model in the objective function formulation and proposes possible solutions to overcome high complexity issues for use in real-time speech/audio algorithms. Specific problems addressed in this dissertation include: 1) the development of approximate but computationally efficient auditory model implementations that are consistent with the principles of psychoacoustics, 2) the development of a mapping scheme that allows synthesizing a time/frequency domain representation from its equivalent auditory model output. The first problem is aimed at addressing the high computational complexity involved in solving perceptual objective functions that require repeated application of auditory model for evaluation of different candidate solutions. In this dissertation, a frequency pruning and a detector pruning algorithm is developed that efficiently implements the various auditory model stages. The performance of the pruned model is compared to that of the original auditory model for different types of test signals in the SQAM database. Experimental results indicate only a 4-7% relative error in loudness while attaining up to 80-90 % reduction in computational complexity. Similarly, a hybrid algorithm is developed specifically for use with sinusoidal signals and employs the proposed auditory pattern combining technique together with a look-up table to store representative auditory patterns. The second problem obtains an estimate of the auditory representation that minimizes a perceptual objective function and transforms the auditory pattern back to its equivalent time/frequency representation. This avoids the repeated application of auditory model stages to test different candidate time/frequency vectors in minimizing perceptual objective functions. In this dissertation, a constrained mapping scheme is developed by linearizing certain auditory model stages that ensures obtaining a time/frequency mapping corresponding to the estimated auditory representation. This paradigm was successfully incorporated in a perceptual speech enhancement algorithm and a sinusoidal component selection task.
ContributorsKrishnamoorthi, Harish (Author) / Spanias, Andreas (Thesis advisor) / Papandreou-Suppappola, Antonia (Committee member) / Tepedelenlioğlu, Cihan (Committee member) / Tsakalis, Konstantinos (Committee member) / Arizona State University (Publisher)
Created2011
Description
Spotlight mode synthetic aperture radar (SAR) imaging involves a tomo- graphic reconstruction from projections, necessitating acquisition of large amounts of data in order to form a moderately sized image. Since typical SAR sensors are hosted on mobile platforms, it is common to have limitations on SAR data acquisi- tion, storage and communication that can lead to data corruption and a resulting degradation of image quality. It is convenient to consider corrupted samples as missing, creating a sparsely sampled aperture. A sparse aperture would also result from compressive sensing, which is a very attractive concept for data intensive sen- sors such as SAR. Recent developments in sparse decomposition algorithms can be applied to the problem of SAR image formation from a sparsely sampled aperture. Two modified sparse decomposition algorithms are developed, based on well known existing algorithms, modified to be practical in application on modest computa- tional resources. The two algorithms are demonstrated on real-world SAR images. Algorithm performance with respect to super-resolution, noise, coherent speckle and target/clutter decomposition is explored. These algorithms yield more accu- rate image reconstruction from sparsely sampled apertures than classical spectral estimators. At the current state of development, sparse image reconstruction using these two algorithms require about two orders of magnitude greater processing time than classical SAR image formation.
ContributorsWerth, Nicholas (Author) / Karam, Lina (Thesis advisor) / Papandreou-Suppappola, Antonia (Committee member) / Spanias, Andreas (Committee member) / Arizona State University (Publisher)
Created2011