The objective of the proposed research is to develop a framework for the signal reconstruction algorithm with sub-Nyquist sampling rate and the low-cost hardware design in system level. A further objective of the proposed research is to monitor the device-under-test (DUT) and to adapt its behaviors. The key contribution of this research is that the high-speed signal acquisition is done by direct subsampling. As the signal is directly sampled without any front-end radio-frequency (RF) components such as mixers or filters, the cost of hardware is reduced. Furthermore, the distortion and the nonlinearity from the RF components can be avoided. The first proposed work is wideband signal reconstruction by dual-rate time-interleaved subsampling hardware and Multi-coset signal reconstruction. Using the combination of the dual-rate hardware and the multi-coset algorithm, the number of sampling channel is significantly reduced compared to the conventional multi-coset works. The second proposed work is jitter tracking by accurate period estimation with incoherent subsampling. In this work, the long-term jitter in PRBS is tracked without hardware synchronization and clock-data-recovery (CDR) circuits. The third proposed work is eye-monitoring and time-domain-reflectometry (TDR) by monobit receiver signal reconstruction. Using a monobit receiver based on incoherent subsampling and time-variant threshold signal, high resolution of reconstructed signal in both amplitude and time is achieved. Compared to a multibit-receiver, the scalability of the test-system is significantly increased.

Safety and reliability are prime concerns in aircraft performance due to the involved costs and risk to lives. Despite the best efforts in design methodology, quality evaluation in production and structural integrity assessment in-service, attainment of one hundred percent safety through development and use of a suitable in-flight health monitoring system is still a farfetched goal. And, evolution of such a system requires, first, identification of an appropriate Technique and next its adoption to meet the challenges posed by newer materials (advanced composites), complex structures and the flight environment. In fact, a quick survey of the available Non-Destructive Evaluation (NDE) techniques suggests Acoustic Emission (AE) as the only available method. High merit in itself could be a weakness - Noise is the worst enemy of AE. So, while difficulties are posed due to the insufficient understanding of the basic behavior of composites, growth and interaction of defects and damage under a specified load condition, high in-flight noise further complicates the issue making the developmental task apparently formidable and challenging. Development of an in-flight monitoring system based on AE to function as an early warning system needs addressing three aspects, viz., the first, discrimination of AE signals from noise data, the second, extraction of required information from AE signals for identification of sources (source characterization) and quantification of its growth, and the third, automation of the entire process. And, a quick assessment of the aspects involved suggests that Artificial Neural Networks (ANN) are ideally suited for solving such a complex problem. A review of the available open literature while indicates a number of investigations carried out using noise elimination and source characterization methods such as frequency filtering and statistical pattern recognition but shows only sporadic attempts using ANN. This may probably be due to the complex nature of the problem involving investigation of a large number of influencing parameters, amount of effort and time to be invested, and facilities required and multi-disciplinary nature of the problem. Hence as stated in the foregoing, the need for such a study cannot be over emphasized. Thus, this thesis is an attempt addressing the issue of analysis and automation of complex sets of AE data such as AE signals mixed with in-flight noise thus forming the first step towards in-flight monitoring using AE. An ANN can in fact replace the traditional algorithmic approaches used in the past. ANN in general are model free estimators and derive their computational efficiency due to large connectivity, massive parallelism, non-linear analog response and learning capabilities. They are better suited than the conventional methods (statistical pattern recognition methods) due to their characteristics such as classification, pattern matching, learning, generalization, fault tolerance and distributed memory and their ability to process unstructured data sets which may be carrying incomplete information at times and hence chosen as the tool. Further, in the current context, the set of investigations undertaken were in the absence of sufficient a priori information and hence clustering of signals generated by AE sources through self-organizing maps is more appropriate. Thus, in the investigations carried out under the scope of this thesis, at first a hybrid network named "NAEDA" (Neural network for Acoustic Emission Data Analysis) using Kohonen self-organizing feature map (KSOM) and multi-layer perceptron (MLP) that learns on back propagation learning rule was specifically developed with innovative data processing techniques built into the network. However, for accurate pattern recognition, multi-layer back propagation NN needed to be trained with source and noise clusters as input data. Thus, in addition to optimizing the network architecture and training parameters, preprocessing of input data to the network and multi-class clustering and classification proved to be the corner stones in obtaining excellent identification accuracy. Next, in-flight noise environment of an aircraft was generated off line through carefully designed simulation experiments carried out in the laboratory (Ex: EMI, friction, fretting and other mechanical and hydraulic phenomena) based on the in-flight noise survey carried out by earlier investigators. From these experiments data was acquired and classified into their respective classes through MLP. Further, these noises were mixed together and clustered through KSOM and then classified into their respective clusters through MLP resulting in an accuracy of 95%- 100% Subsequently, to evaluate the utility of NAEDA for source classification and characterization, carbon fiber reinforced plastic (CFRP) specimens were subjected to spectrum loading simulating typical in-flight load and AE signals were acquired continuously up to a maximum of three designed lives and in some cases up to failure. Further, AE signals with similar characteristics were grouped into individual clusters through self-organizing map and labeled as belonging to appropriate failure modes, there by generating the class configuration. Then MLP was trained with this class information, which resulted in automatic identification and classification of failure modes with an accuracy of 95% - 100%. In addition, extraneous noise generated during the experiments was acquired and classified so as to evaluate the presence or absence of such data in the AE data acquired from the CFRP specimens. In the next stage, noise and signals were mixed together at random and were reclassified into their respective classes through supervised training of multi-layer back propagation NN. Initially only noise was discriminated from the AE signals from CFRP failure modes and subsequently both noise discrimination and failure mode identification and classification was carried out resulting in an accuracy of 95% - 100% in most of the cases. Further, extraneous signals mentioned above were classified which indicated the presence of such signals in the AE signals obtained from the CFRP specimen. Thus, having established the basis for noise identification and AE source classification and characterization, two specific examples were considered to evaluate the utility and efficiency of NAEDA. In the first, with the postulation that different basic failure modes in composites have unique AE signatures, the difference in damage generation and progression can be clearly characterized under different loading conditions. To examine this, static compression tests were conducted on a different set of CFRP specimens till failure with continuous AE monitoring and the resulting AE signals were classified through already trained NAEDA. The results obtained shows that the total number of signals obtained were very less when compared to fatigue tests and the specimens failed with hardly any damage growth. Further, NAEDA was able to discriminate the"noise and failure modes in CFRP specimen with the same degree of accuracy with which it has classified such signals obtained from fatigue tests. In the second example, with the same postulate of unique AE signatures for different failure modes, the differences in the complexion of the damage growth and progression should become clearly evident when one considers specimens with different lay up sequences. To examine this, the data was reclassified on the basis of differences in lay up sequences from specimens subjected to fatigue. The results obtained clearly confirmed the postulation. As can be seen from the summary of the work presented in the foregoing paragraphs, the investigations undertaken within the scope of this thesis involve elaborate experimentation, development of tools, acquisition of extensive data and analysis. Never the less, the results obtained were commensurate with the efforts and have been fruitful. Of the useful results that have been obtained, to state in specific, the first is, discrimination of simulated noise sources achieved with significant success but for some overlapping which is not of major concern as far as noises are concerned. Therefore they are grouped into required number of clusters so as to achieve better classification through supervised NN. This proved to be an innovative measure in supervised classification through back propagation NN. The second is the damage characterization in CFRP specimens, which involved imaginative data processing techniques that proved their worth in terms of optimization of various training parameters and resulted in accurate identification through clustering. Labeling of clusters is made possible by marking each signal starting from clustering to final classification through supervised neural network and is achieved through phenomenological correlation combined with ultrasonic imaging. Most rewarding of all is the identification of failure modes (AE signals) mixed in noise into their respective classes. This is a direct consequence of innovative data processing, multi-class clustering and flexibility of grouping various noise signals into suitable number of clusters. Thus, the results obtained and presented in this thesis on NN approach to AE signal analysis clearly establishes the fact that methods and procedures developed can automate detection and identification of failure modes in CFRP composites under hostile environment, which could lead to the development of an in-flight monitoring system.

La déformation des signaux au long de leur trajet de propagation est un des plus importants facteurs qui doivent être considérés à la réception. Ces effets sont dus à des phénomènes comme l’atténuation, la réflexion, la dispersion et le bruit. Alors que les premiers deux phénomènes sont assez facile à surveiller, parce qu’elles affectent l’amplitude, respectivement le retard des signaux, les deux derniers phénomènes sont plus difficiles à contrôler, parce qu’elles changent les paramètres du signal (amplitude, fréquence et phase) de manière totalement dépendante de l’environnement.Dans cette thèse, l’objectif principal est de contribuer à l’analyse des signaux liés aux différents phénomènes physiques, en visant une meilleure compréhension de ces phénomènes, ainsi que l’estimation de leurs paramètres qui sont intéressants de point de vue applicatif. Plusieurs contextes applicatifs ont été investigués dans deux configurations de : active et passive.Pour la configuration active, le premier contexte applicatif consiste en l’étude du phénomène de cavitation dans le domaine de la surveillance de systèmes hydrauliques. La deuxième application de la configuration active est la détection et le suivi des objets immergés sans synchronisation entre les capteurs d’émission et de réception.Pour la configuration passive, nous nous concentrons sur l’analyse des transitoires de pression dans les conduites d’eau en utilisant une méthode non-intrusive ainsi que sur la surveillance des réseaux d’énergie électrique en présence des phénomènes transitoires comme les arcs électriques.Malgré les différences entre les considérations physiques spécifiques à ces applications, nous proposons un modèle mathématique unique pour les signaux issus des deux types de configurations. Le modèle est basé sur l’analyse des récurrences. Avec ce concept, nous proposons une nouvelle approche pour les ondes basées sur l’espace des phases. Cette technique de construction des formes d’ondes présente l’intérêt de conduire à des méthodes de d’investigation active à haut cadence, très utiles pour la surveillance des phénomènes dynamiques.En plus, nous proposons des approches nouvelles pour l’investigation des caractéristiques des signaux. La première est la mesure TDR* (Time Distributed Recurrences) qui quantifie la matrice des récurrences/ distances et qui est utilisée pour la détection des signaux transitoires. La deuxième approche est l’analyse des phases à plusieurs retards et elle est utilisée pour la discrimination entre des signaux avec des paramètres très proches. Finalement, la quantification des lignes diagonales de la matrice des récurrences est proposée comme alternative pour l’analyse des signaux modulés en fréquence.Les travaux présentent les résultats expérimentaux en utilisant les méthodes théorétiques proposées dans cette thèse. Les résultats sont comparés avec des techniques classiques.Des perspectives de ces travaux, tant dans les domaines théorique et qu’applicatif, sont discutés à la fin du mémoire. / Signals’ deformation along their propagation path is among the most important aspect which has to be taken into account at reception. These effects are caused by phenomena like attenuation, reflection, dispersion and noise. Whereas the first two are rather easy to monitor, because they affect the amplitude, respectively the delay, the latter two are more difficult to control, because they change signals’ parameters (amplitude, frequency and phase) in an environment-dependent manner.In this thesis, the main objective is to contribute to the analysis of signals related to different physical phenomena, aiming to better understand them as well as to estimate their parameters that are interesting from application point of view. Different applicative contexts have been investigated in active and passive sensing configurations. For the active part, we mention the monitoring of cavitation phenomena and its characterization for hydraulic system surveillance. The second application of the active sensing is the underwater object detection and tracking without synchronization between sensors. For the passive configuration, we focus on the pressure transient analysis in water pipes investigation with a non-intrusive method and on the surveillance of electrical power systems in the presence of transient phenomena such as electrical arcs.Despite the differences between the physical considerations, we propose a unique mathematical model of the signals issued from the active/passive sensing system used to analyze the considered phenomena. This model is based on the Recurrence Plot Analysis (RPA) method. With this concept, we propose the phase-space based waveform design. This waveform design technique presents the interest to conduct to a high speed sensing methods, very useful to monitor dynamic phenomena.Moreover, we propose new tools for the investigation of the signals characteristics. The first one is the TDR* measure (Time Distributed Recurrences) that quantifies the recurrence/ distance matrix and it is used for the detection of transient signals. The second one is the multi-lag phase analysis using multiple lags and it is successfully used to discriminate between signals with close parameters. Finally, the diagonal lines quantification of RPA matrix is proposed as an alternative for the analysis of modulated signals.Our work presents the experimental results using the proposed theoretical methods introduced by this thesis. The results are compared with classical techniques.The perspectives of this thesis are presented at the end of this paper.