Classification tasks are an integral part of science, industry, medicine, and business; being such a pervasive technique, its smallest improvement is valuable. Artificial Neural Network (ANN) is one of the strongest techniques used in many disciplines for classification. The ANN technique suffers from drawbacks such as intransparency in spite of its high prediction power. In this dissertation, motivated by learning styles in human brains, ANN’s shortcomings are assuaged and its learning power is improved. Self-Organizing Map (SOM), an ANN variation which has strong unsupervised power, and Feedforward ANN, traditionally used for classification tasks, are hybridized to solidify their benefits and help remove their limitations. These benefits are in two directions: enhancing ANN’s learning power, and improving decision-making. First, the proposed method, named Self-Organizing Error-Driven (SOED) Artificial Neural Network (ANN), shows significant improvements in comparison with usual ANNs. We show SOED is a more accurate, more reliable, and more transparent technique through experimentation with five famous benchmark datasets. Second, the hybridization creates space for inclusion of decision-making goals at the level of ANN’s learning. This gives the classifier the opportunity to handle the inconclusiveness of the data smarter and in the direction of decision-making goals. Through three case studies, naming 1) churn decision analytics, 2) breast cancer diagnosis, and 3) quality control decision making through thermal monitoring of additive manufacturing processes, this novel and cost-sensitive aspect of SOED has been explored and lead to much quantified improvement in decision-making.

The term Rogue Trading is defined as the activity of someone at a financial organisation losing a large amount of money in bad or illegal transactions and trying to hide this. The activity of Rogue traders exposes financial organisations to huge risks and may lead to the organisation collapsing, which will affect other stakeholders like, for example, the customers. In order to detect potential Rogue Trading cases, Control Systems that monitor the employees and the positions they take on financial markets must exist. In this study, a two-step control system is suggested to monitor the margins on Foreign exchange (FX) Forwards traded by employees at the Swedish bank Skandinaviska Enskilda Banken (SEB). The first step in the control system uses a Deep Learning neural network trained on transactional data to predict the margin. The errors of the predictions versus the actual values are then in the second step of the control system used to find outliers which should be flagged for further investigation due to a too high deviation. The results show that the model hopefully can decrease the number of false positives yielded by the current Control Systems at SEB and thus reduce manual inspection of flagged transactions. / Termen Rouge Trading definieras som en aktivitet där någon på en finansiell institution förlorar stora mängder pengar i dåliga eller illegala transaktioner och försöker dölja detta. Detta är något som skapar enorma risker för finansiella institutioner och som kan förorsaka organisationens kollaps, som kan påverka intressenter som till exempel kunder. För att upptäcka potentiella företeelser av Rouge Trading så måste kontrollsystem som övervakar anställda och deras positioner existera. I denna studie föreslås och presenteras ett tvåstegs-system för att övervaka marginaler vid terminsaffärer i utländsk valuta vid Skandinaviska Enskilda Banken (SEB). Det första steget i kontrollsystemet använder ett neuralt närverk tränat på data från transaktioner för att prediktera en marginal. Differenserna mellan prediktionen och det faktiska värdet används för att finna outliers vilka borde flaggas för vidare undersökning. Resultaten visar att modellen förhoppningsvis kan minska antalet falska positiva som det nuvarande kontrollsystemet ger på SEB, något som således kan minska den manuella inspektionen av flaggade transaktioner.

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.

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Dans l’industrie de fabrication mécanique et notamment pour l’utilisation des centres d’usinage haute vitesse, la connaissance des propriétés dynamiques du système broche-outil-pièce en opération est d’une grande importance. L’accroissement des performances des machines-outils et des outils de coupe a œuvré au développement de ce procédé compétitif. D’innombrables travaux ont été menés pour accroître les performances et les remarquables avancées dans les matériaux, les revêtements des outils coupants et les lubrifiants ont permis d’accroître considérablement les vitesses de coupe tout en améliorant la qualité de la surface usinée. Cependant, l’utilisation rationnelle de cette technologie est encore fortement pénalisée par les lacunes dans la connaissance de la coupe, que ce soit au niveau microscopique des interactions fines entre l’outil et la matière coupée, aussi bien qu’au niveau macroscopique intégrant le comportement de la cellule élémentaire d’usinage, si bien que le comportement dynamique en coupe garde encore une grande part de questionnement et exige de l’utilisateur un bon niveau de savoir-faire et parfois d’empirisme pour exploiter au mieux les capacités des moyens de production. Le fonctionnement des machines d’usinage engendre des vibrations qui sont souvent la cause des dysfonctionnements et accélère l’usure des composantes mécaniques (roulements) et outils. Ces vibrations sont une image des efforts internes des systèmes, d’où l’intérêt d’analyser les grandeurs mécaniques vibratoires telle que la vitesse ou l’accélération vibratoire. Ces outils sont indispensables pour une maintenance moderne dont l’objectif est de réduire les coûts liés aux pannes / In machining field, chatter phenomenon takes a lot of interest because manufacturing enterprises are turning to the automation system and the development of reliable and robust monitoring system to provide increased productivity, improved part quality and reduced costs. Chatter occurrence has several negatives effects: a) Poor surface quality, b) Unacceptable inaccuracy, c) Excessive noise, d) Machine tool damage, e) Reduced material removal rate, f) Increase costs in terms of production time, g) Waste of material, h) Environmental impact in terms of materials and energy. Moreover, chatter monitoring is not an easy task for various reasons. Firstly, the non linearity of machining processes and the time-varying of systems complicate this task. Secondly, the sensitivity and the dependency of acquired signals from sensors on different factors, such as machining condition, cutting tool geometry and workpiece material. Thirdly, at high rotating speeds, the gyroscopic effects on the spindle dynamics in addition to the centrifugal force on the bearings and thermal effects become more relevant thus affecting the stability of the system. For these reasons, demands for an advanced automatic chatter detection and monitoring system for optimizing and controlling machining processes becomes a topic of enormous interest. Several researches in this field are performed. Advanced monitoring and detection methods are developed mostly relying on time, frequency and time-frequency analysis. In order to detect chatter in milling centers, three new methods are studied and developed using advanced techniques of signal processing and exploiting cyclostationarity property of signals acquired