This research presents a complete review of signal processing techniques used, today, in vibration based industrial condition monitoring and diagnostics. It also introduces two novel techniques to this field, namely: the Kolmogorov-Smirnov test and Volterra series, which have not yet been applied to vibration based condition monitoring. The first technique, the Kolmogorov-Smirnov test, relies on a statistical comparison of the cumulative probability distribution functions (CDF) from two time series. It must be emphasised that this is not a moment technique, and it uses the whole CDF, in the comparison process. The second tool suggested in this research is the Volterra series. This is a non-linear signal processing technique, which can be used to model a time series. The parameters of this model are used for condition monitoring applications. Finally, this work also presents a comprehensive comparative study between these new methods and the existing techniques. This study is based on results from numerical and experimental applications of each technique here discussed. The concluding remarks include suggestions on how the novel techniques proposed here can be improved.

The need for high spectral efficiency and data rate drives the modulation schemes like OFDM and QAM. The resulting signal is a complex signal with high peak to average power ratio. This property causes signal sensitivity to the non-linearity of power amplifiers. Power amplifiers create out-of-band distortion, in band distortion and spectral re-growth. The spectral re-growth affects the adjacent channels and cause Adjacent Channel Interference (ACI). So linearization techniques should be used to remove the skirts produced by the amplifier in the adjacent frequencies. The objective of this thesis is to figure out a pre distortion method that is simple enough to implement with an analog circuit. We are proposing a novel method to model the non linearity and use the same model as post-distorter and pre-distorter to invert non linearity so linear gain is maintained. The implementation is generic to all the non linear systems and can be implemented to invert the non linearity of any such system.

No description available.

Orientador: João Marcos Travassos Romano / Tese (doutorado) - Universidade Estadual de Campinas, Faculdade de Engenharia Eletrica e de Computação / Made available in DSpace on 2018-08-04T07:52:07Z (GMT). No. of bitstreams: 1 Attux_RomisRibeirodeFaissol_D.pdf: 4889703 bytes, checksum: 022efe9d9ffb4a1629612763bc2c2b3d (MD5) Previous issue date: 2005 / Resumo: O objetivo deste trabalho é investigar a aplicação de estruturas não-lineares e de técnicas de otimização baseadas em computação evolutiva a problemas de equalização e identificação de canal. O relato se divide em duas partes: a primeira voltada à análise dos fundamentos do problema de filtragem, e a segunda, à apresentação de novas abordagens para sua solução. A primeira parte, inaugurada pelas noções primordiais de comunicação, abrange os diferentes aspectos do projeto de um filtro. Permeia toda a exposição uma idéia fundamental: o estabelecimento de um paradigma genérico de filtragem adaptativa. Na segunda parte, apresentamos contribuições originais que se encaixam de diversas formas no espírito desse paradigma. Os problemas abordados são: equalização linear cega, equalização e pré-distorção baseadas em redes neurais, identificação cega, identificação de plantas recursivas, busca cega do receptor de máxima verossimilhança e equalização não-linear cega baseada em predição. Tais propostas, além de possuírem um valor intrínseco, podem ser entendidas como um corpus de evidências da validade das idéias unificadoras pertencentes ao arcabouço teórico erigido / Abstract: The objective of this work is to investigate the use of nonlinear structures and optimization techniques based on evolutionary computation in channel equalization and identification problems. The text is structured according to a twofold division: the first part is dedicated to the analysis of the foundations of the filtering problem, and the second, to the exposition of new approaches to its solution. The first part, whose starting point is the primordial idea of communication, encompasses the various aspects of the design of a signal-processing device. The entire explanation is permeated by a fundamental idea: the constitution of a generic paradigm for adaptive filtering. In the second part, a number of original contributions are presented that are related in various ways to the essence of this paradigm. These contributions involve the problems of blind linear equalization, neural network-based equalization and pre-distortion, blind and supervised identification of feedforward and feedback plants, maximum-likelihood sequence estimation and nonlinear blind equalization based on prediction. Such proposals, apart from possessing an intrinsic value, can also be understood as a corpus of evidences of the validity of the unifying ideas belonging to the established theoretical framework / Doutorado / Telecomunicações e Telemática / Doutor em Engenharia Elétrica

Volterra Series (VS) is often used in the analysis of mildly nonlinear circuits. In this approach, nonlinear circuit analysis is converted into the analysis of a series of linear circuits. The main benefit of this approach is that linear circuit analysis is well established and direct frequency domain analysis of a nonlinear circuit becomes possible. Sensitivity analysis is useful in comparing the quality of two designs and the evaluation of gradient, Jacobian or Hessian matrices, in analog Computer Aided Design. This thesis presents, for the first time, the sensitivity analysis of mildly nonlinear circuits in the frequency domain as an extension of the VS approach. To overcome efficiency limitation due to multiple mixing effects, Nonlinear Transfer Matrix (NTM) is introduced. It is the first explicit analytical representation of the complicated multiple mixing effects. The application of NTM in sensitivity analysis is capable of two orders of magnitude speedup. Per-element distortion decomposition determines the contribution towards the total distortion from an individual nonlinearity. It is useful in design optimization, symbolic simplification and nonlinear model reduction. In this thesis, a numerical distortion decomposition technique is introduced which combines the insight of traditional symbolic analysis with the numerical advantages of SPICE like simulators. The use of NTM leads to an efficient implementation. The proposed method greatly extends the size of the circuit and the complexity of the transistor model over what previous approaches could handle. For example, industry standard compact model, such as BSIM3V3 [35] was used for the first time in distortion analysis. The decomposition can be achieved at device, transistor and block level, all with device level accuracy. The theories have been implemented in a computer program and validated on examples. The proposed methods will leverage the performance of present VS based distortion analysis to the next level.

This thesis has sought to provide another look at RF interference at the fundamental level. While previous interference control and regulation methods have existed in the literature, they were more focused on preventing the interference from happening. On the contrary, we have taken a different approach of correcting the interference once it has happened. This allows the transmitters to be more nonlinear, passive filter design to be eased, and receivers to be aware of interference problems. Under this unifying theme of building intelligent radios where receivers are more cognizant of the transmission environment, we have presented a number of architectures.

Thesis (Ph. D.)--Carleton University, 2004. / Includes bibliographical references (p. 217-223). Also available in electronic format on the Internet.

Orientador : Wagner Caradori do Amaral / Tese (doutorado) - Universidade Estadual de Campinas, Faculdade de Engenharia Eletrica e de Computação / Made available in DSpace on 2018-08-02T05:38:50Z (GMT). No. of bitstreams: 1 Campello_RicardoJoseGabrielliBarreto_D.pdf: 2631759 bytes, checksum: 149a2466bf0190a5ae28f2819f5db427 (MD5) Previous issue date: 2002 / Doutorado

Les modèles linéaires non entiers ont prouvé leur efficacité dans la modélisation de la diffusion thermique pour de faibles variations de température. Cependant, pour de grandes variations de température, les paramètres thermiques dépendent de la température. Par conséquent, la diffusion thermique est régie par un modèle non linéaire non entier. Dans cette thèse, une classe de modèles non linéaires non entiers, basée sur les séries de Volterra non entières, est proposée. Les paramètres non linéaires, tels que les s^n-pôles et l’ordre commensurable, sont estimés au même titre que les coefficients linéaires. Ensuite, le comportement thermique d’un échantillon de fer ARMCO est modélisé pour de grandes variations de température. / Linear fractional differentiation models have proven their efficacy in modeling thermaldiffusive phenomena for small temperature variations. However, for large temperature variations,the thermal parameters are no longer constant but vary along with the temperatureitself. Consequently, the thermal system could be modeled by non linear fractional differentialmodels. Volterra series are first extended to fractional derivatives. Volterra seriesare then used for modeling a non linear thermal system, constituted of an ARMCO iron sample,for large temperature variations.

Breaking the anonymity of different wireless users with the purpose of decreasing internet crime rates is addressed in this thesis by considering radiometric identification techniques. Minute imperfections and non-idealities in the different transmitter components, especially the inherent nonlinearity in power amplifiers, result in variations in their Volterra series representations which could be utilized as a signature. For a two user scenario, signal processing algorithms based on generalized likelihood ratio test(GLRT) and the classical likelihood ratio test are introduced and the resulting receiver decision rules and performance curves are presented. These algorithms consider the high signal to noise ratio(SNR) case where we have available the input samples completely at the receiver which is a practical assumption for most cases. Volterra series are widely used in behavioral modeling of power amplifiers. To validate the existence of these variations in the Volterra series representation of power amplifiers, process variations are introduced as major sources. The plausibility of our techniques are justified by deriving and comparing the Volterra coefficients for the fast and slow process corners. Finally,an information theoretic framework is presented where the amount of mutual information of the output about the Volterra coefficients represents the amount of anonymity taken from users. Here, some results for the low SNR case are presented to prove the achievability of some information about individual systems using our hardware anonymity breaking techniques.