As the failure of a bearing could cause cascading breakdowns of the mechanical system and then lead to costly repairs and production delays, bearing condition monitoring has received much attention for decades. One of the primary methods for this purpose is based on the analysis of vibration signal measured by accelerometers because such data are information-rich. The vibration signal collected from a defective bearing is, however, a mixture of several signal components including the fault-generated impulses, interferences from other machine components, and background noise, where fault-induced impulses are further modulated by various low frequency signal contents. The compounded effects of interferences, background noise and the combined modulation effects make it difficult to detect bearing faults. This is further complicated by the nonstationary nature of vibration signals due to speed variations in some cases, such as the bearings in a wind turbine. As such, the main challenges in the vibration-based bearing monitoring are how to address the modulation, noise, interference, and nonstationarity matters. Over the past few decades, considerable research activities have been carried out to deal with the first three issues. Recently, the nonstationarity matter has also attracted strong interests from both industry and academic community. Nevertheless, the existing techniques still have problems (deficiencies) as listed below: (1) The existing enveloping methods for bearing fault feature extraction are often adversely affected by multiple interferences. To eliminate the effect of interferences, the prefiltering is required, which is often parameter-dependent and knowledge-demanding. The selection of proper filter parameters is challenging and even more so in a time-varying environment. (2) Even though filters are properly designed, they are of little use in handling in-band noise and interferences which are also barriers for bearing fault detection, particularly for incipient bearing faults with weak signatures. (3) Conventional approaches for bearing fault detection under constant speed are no longer applicable to the variable speed case because such speed fluctuations may cause “smearing” of the discrete frequencies in the frequency representation. Most current methods for rotating machinery condition monitoring under time-varying speed require signal resampling based on the shaft rotating frequency. For the bearing case, the shaft rotating frequency is, however, often unavailable as it is coupled with the instantaneous fault characteristic frequency (IFCF) by a fault characteristic coefficient (FCC) which cannot be determined without knowing the fault type. Additionally, the effectiveness of resampling-based methods is largely dependent on the accuracy of resampling procedure which, even if reliable, can complicate the entire fault detection process substantially. (4) Time-frequency analysis (TFA) has proved to be a powerful tool in analyzing nonstationary signal and moreover does not require resampling for bearing fault identification. However, the diffusion of time-frequency representation (TFR) along time and frequency axes caused by lack of energy concentration would handicap the application of the TFA. In fact, the reported TFA applications in bearing fault diagnosis are still very limited. To address the first two aforementioned problems, i.e., (1) and (2), for constant speed cases, two morphology-based methods are proposed to extract bearing fault feature without prefiltering. Another two methods are developed to specifically handle the remaining problems for the bearing fault detection under time-varying speed conditions. These methods are itemized as follows: (1) An efficient enveloping method based on signal Fractal Dimension (FD) for bearing fault feature extraction without prefiltering, (2) A signal decomposition technique based on oscillatory behaviors for noise reduction and interferences removal (including in-band ones), (3) A prefiltering-free and resampling-free approach for bearing fault diagnosis under variable speed condition via the joint application of FD-based envelope demodulation and generalized demodulation (GD), and (4) A combined dual-demodulation transform (DDT) and synchrosqueezing approach for TFR energy concentration level enhancement and bearing fault identification. With respect to constant speed cases, the FD-based enveloping method, where a short time Fractal dimension (STFD) transform is proposed, can suppress interferences and highlight the fault-induced impulsive signature by transforming the vibration signal into a STFD representation. Its effectiveness, however, deteriorates with the increased complexity of the interference frequencies, particularly for multiple interferences with high frequencies. As such, the second method, which isolates fault-induced transients from interferences and noise via oscillatory behavior analysis, is then developed to complement the FD-based enveloping approach. Both methods are independent of frequency information and free from prefiltering, hence eliminating the tedious process for filter parameter specification. The in-band vibration interferences can also be suppressed mainly by the second approach. For the nonstationary cases, a prefiltering-free and resampling-free strategy is developed via the joint application of STFD and GD, from which a resampling-free order spectrum can be derived. This order spectrum can effectively reveal not only the existence of a fault but also its location. However, the success of this method relies largely on an effective enveloping technique. To address this matter and at the same time to exploit the advantages of TFA in nonstationary signal analysis, a TFA technique, involving dual demodulations and an iterative process, is developed and innovatively applied to bearing fault identification. The proposed methods have been validated using both simulation and experimental data collected in our lab. The test results have shown that the first two methods can effectively extract fault signatures, remove the interferences (including in-band ones) without prefiltering, and detect fault types from vibration signals for constant speed cases. The last two have shown to be effective in detecting faults and discern fault types from vibration data collected under variable speed conditions without resampling and prefiltering.

Society is increasingly concerned about the environmental impact of energy systems, and prefers to locate power lines underground. In future, certain socially/environmentally sensitive overhead transmission feeders will need to include underground cable sections. Fault location, especially when using travelling waves, become complicated when the combined transmission line includes a number of discontinuities, such as junction points, teed points and fault points. Consequently, a diverse range of fault locators were developed in this thesis, and the performance of the proposed fault locators investigated. For a combined transmission line (CTL), consisting of one or more overhead line sections and one or more underground cable sections, a hybrid fault location scheme is proposed. This utilises the robustness of an impedance based distance algorithm and the accuracy, but stability concerns, of a travelling wave based fault locator, to determine the faulted section. The distance algorithm can determine the approximate fault location, but if the fault is located near an “underground-overhead” junction point, the accuracy is not sufficient to decide whether the fault is located on the overhead or the underground section. This thesis proposes utilizing a single end travelling wave fault locator to improve the accuracy of the fault location decision. The single end travelling wave fault locator can determine the fault section according to the permutation of the polarity of the “special surges”, which is especially important when the fault is close to a junction point. However, this single end fault locator fails in certain “blind” areas, wand these require the use of a distance relay to help determine fault section. Simulation results demonstrated that this hybrid fault locator can reliably determine which section of the feeder is faulty. For all types of CTL, including teed networks, the multiple-end travelling wave fault locator, utilising the arrival time at the feeder ends of the first fault instigated surges, can estimate the fault location. One of the main features of the proposed fault locator is the classification of the time difference between the arrivals of a fault instigated surge at the feeder ends as standard values, when the fault is located at each of the junction points or teed points. Comparing the time differences measured during an actual fault with these standard values allows the faulted feeder section to be estimated. The simulation results show this multiple-end travelling wave fault locator is highly reliable and suitable for application on combined overhead and underground transmission lines.

Analog technology has been around for a long time. The use of analog technology is necessary since we live in an analog world. However, the transmission and storage of analog technology is more complicated and in many cases less efficient than digital technology. Digital technology, on the other hand, provides fast means to be transmitted and stored. Digital technology continues to grow and it is more widely used than ever before. However, with the advent of new technology that can reproduce digital documents or images with unprecedented accuracy, it poses a risk to the intellectual rights of many artists and also on personal security. One way to protect intellectual rights of digital works is by embedding watermarks in them. The watermarks can be visible or invisible depending on the application and the final objective of the intellectual work. This thesis deals with watermarking images in the discrete wavelet transform domain. The watermarking process was done using the JPEG2000 compression standard as a platform. The hardware implementation was achieved using the ALTERA DSP Builder and SIMULINK software to program the DE2 ALTERA FPGA board. The JPEG2000 color transform and the wavelet transformation blocks were implemented using the hardware-in-the-loop (HIL) configuration.

This dissertation investigates the effectiveness of a two stage fault identification methodology for rotating machines operating under non-stationary conditions with the use of a single vibration transducer. The proposed methodology transforms the machine vibration signal into a discrepancy signal by means of smart feature selection and statistical models. The discrepancy signal indicates the angular position and relative magnitude of irregular signal patterns which are assumed to be indicative of gear faults. The discrepancy signal is also independent of healthy vibration components, such as the meshing frequency, and effects of fluctuating operating conditions. The use of the discrepancy signal significantly reduces the complexity of fault detection and diagnosis. The first stage of the methodology involves extracting smart instantaneous operating condition specific features, while the second stage requires extracting smart instantaneous fault sensitive features. The instantaneous operating condition features are extracted from the coefficients of the low frequency region of the STFT of the vibration signal, since they are sensitive to operating condition changes and robust to the presence of faults. Then the sequence of operating conditions are classified using a hidden Markov model (HMM). The instantaneous fault features are then extracted from the coefficients in the wavelet packet transform (WPT) around the natural frequencies of the gearbox. These features are the converse to the operating condition features,since they are sensitive to the presence of faults and robust to the fluctuating operating conditions. The instantaneous fault features are sent to a set of Gaussian mixture models (GMMs), one GMM for each identified operating condition which enables the instantaneous fault features to be evaluated with respect to their operating condition. The GMMs generate a discrepancy signal, in the angular domain, from which gear faults may be detected and diagnosed by means of simple analysis techniques. The proposed methodology is validated using experimental data from an accelerated life test of a gearbox operated under fluctuating load and speed conditions. / Dissertation (MEng)--University of Pretoria, 2015. / Mechanical and Aeronautical Engineering / Unrestricted

Fault location within electric power systems is an important topic that helps reduce outage duration and increases reliability of the system. This paper explores the topic of fault location using traveling waves generated by fault conditions and the discrete wavelet transform used for time-frequency analysis. The single-ended and double-ended traveling wave methods are presented and evaluated on a single circuit and double circuit 500kV system modeled using MATLAB SIMULINK. Results are compared on the basis of wavelet used for analysis, sampling rate, and fault resistance.

Stethoscopes are the most commonly used medical devices for diagnosing heart conditions because they are inexpensive, noninvasive, and light enough to be carried around by a clinician. Auscultation with a stethoscope requires considerable skill and experience, but the introduction of digital stethoscopes allows for the automation of this task. Auscultation waveform segmentation, which is the process of determining the boundaries of heart sound and murmur segments, is the primary challenge in automating the diagnosis of various heart conditions. The purpose of this thesis is to improve the accuracy and efficiency of established techniques for detecting, segmenting, and classifying heart sounds and murmurs in digitized phonocardiogram audio files. Two separate segmentation techniques based on the discrete wavelet transform (DWT) and the simplicity transform are integrated into a MATLAB software system that is capable of automatically detecting and classifying sound segments. The performance of the two segmentation methods for recognizing normal heart sounds and several different heart murmurs is compared by quantifying the results with clinical and technical metrics. The two clinical metrics are the false negative detection rate (FNDR) and the false positive detection rate (FPDR), which count heart cycles rather than sound segments. The wavelet and simplicity methods have a 4% and 9% respective FNDR, so it is unlikely that either method would not detect a heart condition. However, the 22% and 0% respective FPDR signifies that the wavelet method is likely to detect false heart conditions, while the simplicity method is not. The two technical metrics are the true murmur detection rate (TMDR) and the false murmur detection rate (FMDR), which count sound segments rather than heart cycles. Both methods are equally likely to detect true murmurs given their 83% TMDR. However, the 13% and 0% respective FMDR implies that the wavelet method is susceptible to detecting false murmurs, while the simplicity method is not. Simplicity-based segmentation, therefore, demonstrates superior performance to wavelet-based segmentation, as both are equally likely to detect true murmurs, but only the simplicity method has no chance of detecting false murmurs.

Tato práce se zabývá možností využití vlnkové transformace v aplikacích, které se zabývají potlačením šumu. Především se jedná o oblast filtrace signálu EKG. Úkolem je zhodnotit vliv různých parametrů nastavení samotné filtrace a zjistit jaký vliv má různé nastavení prahování wavelet koeficientů. Výsledkem práce je také stanovení hodnot prahů, stanovení nejlepšího způsobu rozkladu signálu a volba rekonstrukčních bank filtrů. Text obsahuje výsledky Wienerovy filtrace, při které byly testovány různé banky rozkladových a rekonstrukčních filtrů.Všechny popsané filtrační metody byly testovány na reálných záznamech EKG s aditivním myopotenciálním šumem. Algoritmy byly realizovány v prostředí MATLAB.

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Signal compression is daily-used tool for memory capacities reduction and for fast data communication. Methods based on wavelet transform seem to be very effective nowadays. Signal decomposition with a suitable bank filters following with coefficients quantization represents one of the available technique. After packing quantized coefficients into one sequence, run length coding together with Huffman coding are implemented. This thesis focuses on compression effectiveness for the different wavelet transform and quantization settings.

This diploma thesis focuses on current possibilities concerning the employment of wavelet transformation for video signal compression. One part of the work is devoted to the necessary execution of this task in practise. This deals with video signal and its features description, wavelet transformation and compression methods. The second part concentrates on description of selected compression method. It is the SPIHT (Set Partitioning In Hierarchical Trees) algorithm which is intended for compression of static image data. The algorithm is modified for usage with video signal compression which is specific for its time redundancy. The algorithm is called 3D SPIHT as it works in the spatial and time domain. The algorithm is implemented in the MATLAB programming environment which provides a sophisticated support for wavelet transformation (Wavelet Toolbox). To provide a simple and intuitive encoder control there has been developed an application delivering graphical user interface (GUI). On displayed image previews and measured graphs the user can change encoder parameters and monitor performed changes. There are four image test-sequential modes containing various scenes with different features. The final part of the work is focused on testing of the proposed encoding scheme, various image test-sequential modes and encoder settings. Measured values are graphically displayed and analyzed.