Unconditionally convergent time domain adaptive and time-frequency techniques for epicyclic gearbox vibration

Condition monitoring of epicyclic gearboxes through vibration signature analysis, with particular focus on time domain methods and the use of adaptive filtering techniques for the purpose of signal enhancement, is the central theme of this work. Time domain filtering methods for the purpose of removal of random noise components from periodic, but not necessarily stationary or cyclostationary, signals are developed. Damage identification is accomplished through vibration signature analysis by nonstationary timefrequency methods, belonging to Cohen’s general class of time-frequency distributions, strictly based in the time domain. Although a powerful and commonly used noise reduction technique, synchronous averaging requires alternate sensors in addition to the vibration pickup. For this reason the use of time domain techniques that employ only the vibration data is investigated. Adaptive filters may be used to remove random noise from the nonstationary signals considered. The well-known Least Mean Squares algorithm is employed in an adaptive line enhancer configuration. To counter the much discussed convergence difficulties that are often experienced when the least mean squares algorithm is applied, a new unconditionally convergent algorithm based on the spherical quadratic steepest descent method is presented. The spherical quadratic steepest descent method has been shown to be unconditionally convergent when applied to a quadratic objective function. Time-frequency methods are succinctly employed to analyse the vibration signals simultaneously in the time and frequency domains. Transients covering a wide frequency range are a clear and definite indication of impacting events as gear teeth mate, and observation of such events on a timefrequency distribution are used to indicate damage to the transmission. The pseudo Wigner-Ville distribution and the Spectrogram, both belonging to Cohen’s general class of time-frequency distributions are comparatively used to the end of damage identification. It is shown that an unconditionally convergent adaptive filtering technique used in conjunction with time-frequency methods can indicate a damaged condition in an epicyclic gearbox, where the non-adaptively filtered data did not present clear indications of damage.Dissertation (MEng (Mechanical Engineering))--University of Pretoria, 2007.Mechanical and Aeronautical EngineeringMEngMEngunrestricte

Adaptive noise cancelling and time–frequency techniques for rail surface defect detection

Adaptive noise cancelling (ANC) is a technique which is very effective to remove additive noises from the contaminated signals. It has been widely used in the fields of telecommunication, radar and sonar signal processing. However it was seldom used for the surveillance and diagnosis of mechanical systems before late of 1990s. As a promising technique it has gradually been exploited for the purpose of condition monitoring and fault diagnosis. Time-frequency analysis is another useful tool for condition monitoring and fault diagnosis purpose as time-frequency analysis can keep both time and frequency information simultaneously. This paper presents an ANC and time-frequency application for railway wheel flat and rail surface defect detection. The experimental results from a scaled roller test rig show that this approach can significantly reduce unwanted interferences and extract the weak signals from strong background noises. The combination of ANC and time-frequency analysis may provide us one of useful tools for condition monitoring and fault diagnosis of railway vehicles

Adaptive Signal Processing Techniques and Realistic Propagation Modeling for Multiantenna Vital Sign Estimation

Tämän työn keskeisimpänä tavoitteena on ihmisen elintoimintojen tarkkailu ja estimointi käyttäen radiotaajuisia mittauksia ja adaptiivisia signaalinkäsittelymenetelmiä monen vastaanottimen kantoaaltotutkalla. Työssä esitellään erilaisia adaptiivisia menetelmiä, joiden avulla hengityksen ja sydämen värähtelyn aiheuttamaa micro-Doppler vaihemodulaatiota sisältävät eri vastaanottimien signaalit voidaan yhdistää. Työssä johdetaan lisäksi realistinen malli radiosignaalien etenemiselle ja heijastushäviöille, jota käytettiin moniantennitutkan simuloinnissa esiteltyjen menetelmien vertailemiseksi. Saatujen tulosten perusteella voidaan osoittaa, että adaptiiviset menetelmät parantavat langattoman elintoimintojen estimoinnin luotettavuutta, ja mahdollistavat monitoroinnin myös pienillä signaali-kohinasuhteen arvoilla.This thesis addresses the problem of vital sign estimation through the use of adaptive signal enhancement techniques with multiantenna continuous wave radar. The use of different adaptive processing techniques is proposed in a novel approach to combine signals from multiple receivers carrying the information of the cardiopulmonary micro-Doppler effect caused by breathing and heartbeat. The results are based on extensive simulations using a realistic signal propagation model derived in the thesis. It is shown that these techniques provide a significant increase in vital sign rate estimation accuracy, and enable monitoring at lower SNR conditions

Efficient material characterization by means of the Doppler effect in microwaves

Subject of this thesis is the efficient material characterization and defects detection by means of the Doppler effect with microwaves. The first main goal of the work is to develop a prototype of a microwave Doppler system for Non-Destructive Testing (NDT) purposes. Therefore it is necessary that the Doppler system satisfies the following requirements: non-expensive, easily integrated into industrial process, allows fast measurements. The Doppler system needs to include software for hardware control, measurements, and fast signal processing. The second main goal of the thesis is to establish and experimentally confirm possible practical applications of the Doppler system. The Doppler system consists of the following parts. The hardware part is designed in a way to ensure fast measurement and easy adjustment to the different radar types. The software part of the system contains tools for: hardware control, data acquisition, signal processing and representing data to the user. In this work firstly a new type of 2D Doppler amplitude imaging was developed and formalized. Such a technique is used to derive information about the measured object from several angles of view. In the thesis special attention was paid to the frequency analysis of the mea- sured signals as a means to improve spatial resolution of the radar. In the context of frequency analysis we present 2D Doppler frequency imaging and compare it with amplitude imaging. In the thesis the spatial resolution ability of CW radars is examined and im- proved. We show that the joint frequency and the amplitude signal processing allows to significantly increase the spatial resolution of the radar.Das Thema dieser Dissertation ist die effiziente Materialcharakterisierung und Fehlerdetektion durch Nutzung des Dopplereffektes mittels Mikrowellen. Das erste Hauptziel der Arbeit ist die Entwicklung eines Prototyps eines Mikrowellen-Doppler-Systems im Bereich der zerstörungsfreien Prüfung. Das Doppler-System muss folgenden Voraussetzungen erfüllen: es sollte preisgünstig sein, leicht in industrielle Prozesse integrierbar sein und schnelle Messungen erlauben. Das Doppler-System muss die Software für die Hardware-Kontrolle, den Messablauf und die schnelle Signalverarbeitung beinhalten. Das zweite Hauptziel der Dissertation ist es, mögliche praktische Anwendungsfelder des Doppler-Systems zu identifizieren und experimentell zu bearbeiten. Das Doppler-System besteht aus zwei Teilen. Der Hardware-Teil ist so konstruiert, dass er schnelle Messungen und leichte Anpassungen an verschiedene Sensor- und Radartypen zulässt. Der Software-Teil des Systems beinhaltet Werkzeuge für: Hardware-Kontrolle, Datenerfassung, Signalverarbeitung und Programme, um die Daten für den Benutzer zu präsentieren. In dieser Arbeit wurde zuerst ein neuer Typ der 2D-Doppler-Amplitudenbildgebung entwickelt und formalisiert. Dieser Technik wird dafür benutzt, Informationen über die gemessenen Objekte von verschiedenen Blickpunkten aus zu erhalten. In dieser Doktorarbeit wird der Frequenzanalyse der gemessenen Signale besondere Aufmerksamkeit geschenkt, um die Ortsauflösung des Radars zu verbessern. Im Kontext der Frequenzanalyse wird die 2D-Doppler-Frequenzbildgebung präsentiert und mit der Amplitudenbildgebung vergleichen. In dieser Dissertation werden die räumliche Auflösungsmöglichkeiten von CW-Radaren untersucht und verbessert. Es wird gezeigt, dass es die Frequenz- und Amplitudensignalverarbeitung erlaubt, die Ortsauflösung des Radars erheblich zu erhöhen

Multivariate time-frequency analysis

Recent advances in time-frequency theory have led to the development of high resolution time-frequency algorithms, such as the empirical mode decomposition (EMD) and the synchrosqueezing transform (SST). These algorithms provide enhanced localization in representing time varying oscillatory components over conventional linear and quadratic time-frequency algorithms. However, with the emergence of low cost multichannel sensor technology, multivariate extensions of time-frequency algorithms are needed in order to exploit the inter-channel dependencies that may arise for multivariate data. Applications of this framework range from filtering to the analysis of oscillatory components. To this end, this thesis first seeks to introduce a multivariate extension of the synchrosqueezing transform, so as to identify a set of oscillations common to the multivariate data. Furthermore, a new framework for multivariate time-frequency representations is developed using the proposed multivariate extension of the SST. The performance of the proposed algorithms are demonstrated on a wide variety of both simulated and real world data sets, such as in phase synchrony spectrograms and multivariate signal denoising. Finally, multivariate extensions of the EMD have been developed that capture the inter-channel dependencies in multivariate data. This is achieved by processing such data directly in higher dimensional spaces where they reside, and by accounting for the power imbalance across multivariate data channels that are recorded from real world sensors, thereby preserving the multivariate structure of the data. These optimized performance of such data driven algorithms when processing multivariate data with power imbalances and inter-channel correlations, and is demonstrated on the real world examples of Doppler radar processing.Open Acces

Characterization, Classification, and Genesis of Seismocardiographic Signals

Seismocardiographic (SCG) signals are the acoustic and vibration induced by cardiac activity measured non-invasively at the chest surface. These signals may offer a method for diagnosing and monitoring heart function. Successful classification of SCG signals in health and disease depends on accurate signal characterization and feature extraction. In this study, SCG signal features were extracted in the time, frequency, and time-frequency domains. Different methods for estimating time-frequency features of SCG were investigated. Results suggested that the polynomial chirplet transform outperformed wavelet and short time Fourier transforms. Many factors may contribute to increasing intrasubject SCG variability including subject posture and respiratory phase. In this study, the effect of respiration on SCG signal variability was investigated. Results suggested that SCG waveforms can vary with lung volume, respiratory flow direction, or a combination of these criteria. SCG events were classified into groups belonging to these different respiration phases using classifiers, including artificial neural networks, support vector machines, and random forest. Categorizing SCG events into different groups containing similar events allows more accurate estimation of SCG features. SCG feature points were also identified from simultaneous measurements of SCG and other well-known physiologic signals including electrocardiography, phonocardiography, and echocardiography. Future work may use this information to get more insights into the genesis of SCG

New approaches for EEG signal processing: artifact EOG removal by ICA-RLS scheme and tracks extraction method

Localizing the bioelectric phenomena originating from the cerebral cortex and evoked by auditory and somatosensory stimuli are clear objectives to both understand how the brain works and to recognize different pathologies. Diseases such as Parkinson’s, Alzheimer’s, schizophrenia and epilepsy are intensively studied to find a cure or accurate diagnosis. Epilepsy is considered the disease with major prevalence within disorders with neurological origin. The recurrent and sudden incidence of seizures can lead to dangerous and possibly life-threatening situations. Since disturbance of consciousness and sudden loss of motor control often occur without any warning, the ability to predict epileptic seizures would reduce patients’ anxiety, thus considerably improving quality of life and safety. The common procedure for epilepsy seizure detection is based on brain activity monitorization via electroencephalogram (EEG) data. This process consumes a lot of time, especially in the case of long recordings, but the major problem is the subjective nature of the analysis among specialists when analyzing the same record. From this perspective, the identification of hidden dynamical patterns is necessary because they could provide insight into the underlying physiological mechanisms that occur in the brain. Time-frequency distributions (TFDs) and adaptive methods have demonstrated to be good alternatives in designing systems for detecting neurodegenerative diseases. TFDs are appropriate transformations because they offer the possibility of analyzing relatively long continuous segments of EEG data even when the dynamics of the signal are rapidly changing. On the other hand, most of the detection methods proposed in the literature assume a clean EEG signal free of artifacts or noise, leaving the preprocessing problem opened to any denoising algorithm. In this thesis we have developed two proposals for EEG signal processing: the first approach consists in electrooculogram (EOG) removal method based on a combination of ICA and RLS algorithms which automatically cancels the artifacts produced by eyes movement without the use of external “ad hoc” electrode. This method, called ICA-RLS has been compared with other techniques that are in the state of the art and has shown to be a good alternative for artifacts rejection. The second approach is a novel method in EEG features extraction called tracks extraction (LFE features). This method is based on the TFDs and partial tracking. Our results in pattern extractions related to epileptic seizures have shown that tracks extraction is appropriate in EEG detection and classification tasks, being practical, easily applicable in medical environment and has acceptable computational cost

Tracking Rhythmicity in Biomedical Signals using Sequential Monte Carlo methods

Cyclical patterns are common in signals that originate from natural systems such as the human body and man-made machinery. Often these cyclical patterns are not perfectly periodic. In that case, the signals are called pseudo-periodic or quasi-periodic and can be modeled as a sum of time-varying sinusoids, whose frequencies, phases, and amplitudes change slowly over time. Each time-varying sinusoid represents an individual rhythmical component, called a partial, that can be characterized by three parameters: frequency, phase, and amplitude. Quasi-periodic signals often contain multiple partials that are harmonically related. In that case, the frequencies of other partials become exact integer multiples of that of the slowest partial. These signals are referred to as multi-harmonic signals. Examples of such signals are electrocardiogram (ECG), arterial blood pressure (ABP), and human voice. A Markov process is a mathematical model for a random system whose future and past states are independent conditional on the present state. Multi-harmonic signals can be modeled as a stochastic process with the Markov property. The Markovian representation of multi-harmonic signals enables us to use state-space tracking methods to continuously estimate the frequencies, phases, and amplitudes of the partials. Several research groups have proposed various signal analysis methods such as hidden Markov Models (HMM), short time Fourier transform (STFT), and Wigner-Ville distribution to solve this problem. Recently, a few groups of researchers have proposed Monte Carlo methods which estimate the posterior distribution of the fundamental frequency in multi-harmonic signals sequentially. However, multi-harmonic tracking is more challenging than single-frequency tracking, though the reason for this has not been well understood. The main objectives of this dissertation are to elucidate the fundamental obstacles to multi-harmonic tracking and to develop a reliable multi-harmonic tracker that can track cyclical patterns in multi-harmonic signals

Radio frequency interference detection and mitigation techniques for navigation and Earth observation

Radio-Frequency Interference (RFI) signals are undesired signals that degrade or disrupt the performance of a wireless receiver. RFI signals can be troublesome for any receiver, but they are especially threatening for applications that use very low power signals. This is the case of applications that rely on the Global Navigation Satellite Systems (GNSS), or passive microwave remote sensing applications such as Microwave Radiometry (MWR) and GNSS-Reflectometry (GNSS-R). In order to solve the problem of RFI, RFI-countermeasures are under development. This PhD thesis is devoted to the design, implementation and test of innovative RFI-countermeasures in the fields of MWR and GNSS. In the part devoted to RFI-countermeasures for MWR applications, first, this PhD thesis completes the development of the MERITXELL instrument. The MERITXELL is a multi-frequency total-power radiometer conceived to be an outstanding platform to perform detection, characterization, and localization of RFI signals at the most common MWR imaging bands up to 92 GHz. Moreover, a novel RFI mitigation technique is proposed for MWR: the Multiresolution Fourier Transform (MFT). An assessment of the performance of the MFT has been carried out by comparison with other time-frequency mitigation techniques. According to the results, the MFT technique is a good trade-off solution among all other techniques since it can mitigate efficiently all kinds of RFI signals under evaluation. In the part devoted to RFI-countermeasures for GNSS and GNSS-R applications, first, a system for RFI detection and localization at GNSS bands is proposed. This system is able to detect RFI signals at the L1 band with a sensitivity of -108 dBm at full-band, and of -135 dBm for continuous wave and chirp-like signals when using the averaged spectrum technique. Besides, the Generalized Spectral Separation Coefficient (GSSC) is proposed as a figure of merit to evaluate the Signal-to-Noise Ratio (SNR) degradation in the Delay-Doppler Maps (DDMs) due to the external RFI effect. Furthermore, the FENIX system has been conceived as an innovative system for RFI detection and mitigation and anti-jamming for GNSS and GNSS-R applications. FENIX uses the MFT blanking as a pre-correlation excision tool to perform the mitigation. In addition, FENIX has been designed to be cross-GNSS compatible and RFI-independent. The principles of operation of the MFT blanking algorithm are assessed and compared with other techniques for GNSS signals. Its performance as a mitigation tool is proven using GNSS-R data samples from a real airborne campaign. After that, the main building blocks of the patented architecture of FENIX have been described. The FENIX architecture has been implemented in three real-time prototypes. Moreover, a simulator named FENIX-Sim allows for testing its performance under different jamming scenarios. The real-time performance of FENIX prototype has been tested using different setups. First, a customized VNA has been built in order to measure the transfer function of FENIX in the presence of several representative RFI/jamming signals. The results show how the power transfer function adapts itself to mitigate the RFI/jamming signal. Moreover, several real-time tests with GNSS receivers have been performed using GPS L1 C/A, GPS L2C, and Galileo E1OS. The results show that FENIX provides an extra resilience against RFI and jamming signals up to 30 dB. Furthermore, FENIX is tested using a real GNSS timing setup. Under nominal conditions, when no RFI/jamming signal is present, a small additional jitter on the order of 2-4 ns is introduced in the system. Besides, a maximum bias of 45 ns has been measured under strong jamming conditions (-30 dBm), which is acceptable for current timing systems requiring accuracy levels of 100 ns. Finally, the design of a backup system for GNSS in tracking applications that require high reliability against RFI and jamming attacks is proposed.Les interferències de radiofreqüència (RFI) són senyals no desitjades que degraden o interrompen el funcionament dels receptors sense fils. Les RFI poden suposar un problema per qualsevol receptor, però són especialment amenaçadores per les a aplicacions que fan servir senyals de molt baixa potència. Aquest és el cas de les aplicacions que depenen dels sistemes mundials de navegació per satèl·lit (GNSS) o de les aplicacions de teledetecció passiva de microones, com la radiometria de microones (MWR) i la reflectometria GNSS (GNSS-R). Per combatre aquest problema, sistemes anti-RFI s'estan desenvolupament actualment. Aquesta tesi doctoral està dedicada al disseny, la implementació i el test de sistemes anti-RFI innovadors en els camps de MWR i GNSS. A la part dedicada als sistemes anti-RFI en MWR, aquesta tesi doctoral completa el desenvolupament de l'instrument MERITXELL. El MERITXELL és un radiòmetre multifreqüència concebut com una plataforma excepcional per la detecció, caracterització i localització de RFI a les bandes de MWR més utilitzades per sota dels 92 GHz. A més a més, es proposa una nova tècnica de mitigació de RFI per MWR: la Transformada de Fourier amb Multiresolució (MFT). El funcionament de la MFT s'ha comparat amb el d'altres tècniques de mitigació en els dominis del temps i la freqüència. D'acord amb els resultats obtinguts, la MFT és una bona solució de compromís entre les altres tècniques, ja que pot mitigar de manera eficient tots els tipus de senyals RFI considerats. A la part dedicada als sistemes anti-RFI en GNSS i GNSS-R, primer es proposa un sistema per a la detecció i localització de RFI a les bandes GNSS. Aquest sistema és capaç de detectar senyals RFI a la banda L1 amb una sensibilitat de -108 dBm a tota la banda, i de -135 dBm per a senyals d'ona contínua i chirp fen un mitjana de l'espectre. A més a més, el Coeficient de Separació Espectral Generalitzada (GSSC) es proposa com una mesura per avaluar la degradació de la relació senyal a soroll (SNR) en els Mapes de Delay-Doppler (DDM) a causa del impacte de les RFI. La major contribució d'aquesta tesi doctoral és el sistema FENIX. FENIX és un sistema innovador de detecció i mitigació de RFI i inhibidors de freqüència per aplicacions GNSS i GNSS-R. FENIX utilitza la MFT per eliminar la interferència abans del procés de correlació amb el codi GNSS independentment del tipus de RFI. L'algoritme de mitigació de FENIX s'ha avaluat i comparat amb altres tècniques i els principals components de la seva arquitectura patentada es descriuen. Finalment, un simulador anomenat FENIX-Sim permet avaluar el seu rendiment en diferents escenaris d'interferència. El funcionament en temps real del prototip FENIX ha estat provat utilitzant diferents mètodes. En primer lloc, s'ha creat un analitzador de xarxes per a mesurar la funció de transferència del FENIX en presència de diverses RFI representatives. Els resultats mostren com la funció de transferència s'adapta per mitigar el senyal interferent. A més a més, s'han realitzat diferents proves en temps real amb receptors GNSS compatibles amb els senyals GPS L1 C/A, GPS L2C i Galileo E1OS. Els resultats mostren que FENIX proporciona una resistència addicional contra les RFI i els senyals dels inhibidors de freqüència de fins a 30 dB. A més a més, FENIX s'ha provat amb un sistema comercial de temporització basat en GNSS. En condicions nominals, sense RFI, FENIX introdueix un petit error addicional de tan sols 2-4 ns. Per contra, el biaix màxim mesurat en condicions d'alta interferència (-30 dBm) és de 45 ns, el qual és acceptable per als sistemes de temporització actuals que requereixen nivells de precisió d'uns 100 ns. Finalment, es proposa el disseny d'un sistema robust de seguiment, complementari als GNSS, per a aplicacions que requereixen alta fiabilitat contra RFI.Postprint (published version