A sensitivity comparison of Neuro-fuzzy feature extraction methods from bearing failure signals

This thesis presents an account of investigations made into building bearing fault classifiers for outer race faults (ORF), inner race faults (IRF), ball faults (BF) and no fault (NF) cases using wavelet transforms, statistical parameter features and Artificial Neuro-Fuzzy Inference Systems (ANFIS). The test results showed that the ball fault (BF) classifier successfully achieved 100% accuracy without mis-classification, while the outer race fault (ORF), inner race fault (IRF) and no fault (NF) classifiers achieved mixed results

Automatic classification of power quality disturbances using optimal feature selection based algorithm

The development of renewable energy sources and power electronic converters in conventional power systems leads to Power Quality (PQ) disturbances. This research aims at automatic detection and classification of single and multiple PQ disturbances using a novel optimal feature selection based on Discrete Wavelet Transform (DWT) and Artificial Neural Network (ANN). DWT is used for the extraction of useful features, which are used to distinguish among different PQ disturbances by an ANN classifier. The performance of the classifier solely depends on the feature vector used for the training. Therefore, this research is required for the constructive feature selection based classification system. In this study, an Artificial Bee Colony based Probabilistic Neural Network (ABCPNN) algorithm has been proposed for optimal feature selection. The most common types of single PQ disturbances include sag, swell, interruption, harmonics, oscillatory and impulsive transients, flicker, notch and spikes. Moreover, multiple disturbances consisting of combination of two disturbances are also considered. The DWT with multi-resolution analysis has been applied to decompose the PQ disturbance waveforms into detail and approximation coefficients at level eight using Daubechies wavelet family. Various types of statistical parameters of all the detail and approximation coefficients have been analysed for feature extraction, out of which the optimal features have been selected using ABC algorithm. The performance of the proposed algorithm has been analysed with different architectures of ANN such as multilayer perceptron and radial basis function neural network. The PNN has been found to be the most suitable classifier. The proposed algorithm is tested for both PQ disturbances obtained from the parametric equations and typical power distribution system models using MATLAB/Simulink and PSCAD/EMTDC. The PQ disturbances with uniformly distributed noise ranging from 20 to 50 dB have also been analysed. The experimental results show that the proposed ABC-PNN based approach is capable of efficiently eliminating unnecessary features to improve the accuracy and performance of the classifier

AI and OR in management of operations: history and trends

The last decade has seen a considerable growth in the use of Artificial Intelligence (AI) for operations management with the aim of finding solutions to problems that are increasing in complexity and scale. This paper begins by setting the context for the survey through a historical perspective of OR and AI. An extensive survey of applications of AI techniques for operations management, covering a total of over 1200 papers published from 1995 to 2004 is then presented. The survey utilizes Elsevier's ScienceDirect database as a source. Hence, the survey may not cover all the relevant journals but includes a sufficiently wide range of publications to make it representative of the research in the field. The papers are categorized into four areas of operations management: (a) design, (b) scheduling, (c) process planning and control and (d) quality, maintenance and fault diagnosis. Each of the four areas is categorized in terms of the AI techniques used: genetic algorithms, case-based reasoning, knowledge-based systems, fuzzy logic and hybrid techniques. The trends over the last decade are identified, discussed with respect to expected trends and directions for future work suggested

Insight on Effectiveness of Frequently Exercised PQ Classification Techniques

The growing demands of global consumer market in green energy system have opened the doors for many technologies as well as various sophisticated electrical devices for both commercial and domestic usage. However, with the increasing demands of energy and better quality of services, there is a significant increase in non-linearity in load distribution causing potential effect on the Power Quality (PQ). The harmful effects on PQ are various events e.g. sag, swell, harmonics etc that causes significant amount of system degradation. Therefore, this paper discusses various significant research techniques pertaining to the PQ disturbance classification system introduced by the authors in the past and analyzes its effectiveness scale in terms of research gap. The paper discusses some of the frequently exercised PQ classification techniques from the most relevant literatures in order to have more insights of the techniques

Analyses of Online Monitoring Signals for a GMAW Process Before and After Improvement

The ability to detect the onset of welding instability is a very powerful tool in welding process monitoring and control. Toward this goal, this study investigates a gas metal arc welding (GMAW) process by analyzing online monitoring signals. Two separate data sets are obtained from the process, which correspond to (a) a stable process after improvement and (b) a relatively unstable process which tends to exhibit spatter and poor weld bead geometry. Voltage, current, wire-feeding speed and line speed signals for both data sets are analysed and features are extracted from the raw signals using different signal processing techniques. Specifically, phase diagrams, signal distributions, Fast Fourier transform (FFT) and Wavelet Transform methodologies are implemented. The process parameters differ for the data corresponding to the stable and unstable processes rendering the two data sets incomparable. As such, an overlapping region of parameters is selected and this data is used to develop a multi-layer neural network model. The model uses the features extracted to distinguish between the two data sets under the similar input conditions. The trained model is then used to classify data as being from a stable process or an unstable process. Finally, an ant colony optimization algorithm is used to select the optimal subset of features for the classification model. For this task, fuzzy k-nearest neighbor algorithm is used as the classifier instead due to the computational simplicity. The results indicate that more than one single feature is able to yield 100% classification accuracy alone. A way to rank those features is discussed. Moreover, the effect of window size is also investigated

Robust classification of advanced power quality disturbances in smart grids

The insertion of new devices, increased data flow, intermittent generation and massive computerization have considerably increased current electrical systems’ complexity. This increase resulted in necessary changes, such as the need for more intelligent electrical net works to adapt to this different reality. Artificial Intelligence (AI) plays an important role in society, especially the techniques based on the learning process, and it is extended to the power systems. In the context of Smart Grids (SG), where the information and innovative solutions in monitoring is a primary concern, those techniques based on AI can present several applications. This dissertation investigates the use of advanced signal processing and ML algorithms to create a Robust Classifier of Advanced Power Quality (PQ) Dis turbances in SG. For this purpose, known models of PQ disturbances were generated with random elements to approach real applications. From these models, thousands of signals were generated with the performance of these disturbances. Signal processing techniques using Discrete Wavelet Transform (DWT) were used to extract the signal’s main charac teristics. This research aims to use ML algorithms to classify these data according to their respective features. ML algorithms were trained, validated, and tested. Also, the accuracy and confusion matrix were analyzed, relating the logic behind the results. The stages of data generation, feature extraction and optimization techniques were performed in the MATLAB software. The Classification Learner toolbox was used for training, validation and testing the 27 different ML algorithms and assess each performance. All stages of the work were previously idealized, enabling their correct development and execution. The results show that the Cubic Support Vector Machine (SVM) classifier achieved the maximum accuracy of all algorithms, indicating the effectiveness of the proposed method for classification. Considerations about the results were interpreted as explaining the per formance of each technique, its relations and their respective justifications.A inserção de novos dispositivos na rede, aumento do fluxo de dados, geração intermitente e a informatização massiva aumentaram consideravelmente a complexidade dos sistemas elétricos atuais. Esse aumento resultou em mudanças necessárias, como a necessidade de redes elétricas mais inteligentes para se adaptarem a essa realidade diferente. A nova ger ação de técnicas de Inteligência Artificial, representada pelo "Big Data", Aprendizado de Máquina ("Machine Learning"), Aprendizagem Profunda e Reconhecimento de Padrões representa uma nova era na sociedade e no desenvolvimento global baseado na infor mação e no conhecimento. Com as mais recentes Redes Inteligentes, o uso de técnicas que utilizem esse tipo de inteligência será ainda mais necessário. Esta dissertação investiga o uso de processamento de sinais avançado e também algoritmos de Aprendizagem de Máquina para desenvolver um classificador robusto de distúrbios de qualidade de energia no contexto das Redes Inteligentes. Para isso, modelos já conhecidos de alguns proble mas de qualidade foram gerados junto com ruídos aleatórios para que o sistema fosse similar a aplicações reais. A partir desses modelos, milhares de sinais foram gerados e a Transformada Wavelet Discreta foi usada para extrair as principais características destas perturbações. Esta dissertação tem como objetivo utilizar algoritmos baseados no con ceito de Aprendizado de Máquina para classificar os dados gerados de acordo com suas classes. Todos estes algoritmos foram treinados, validados e por fim, testados. Além disso, a acurácia e a matriz de confusão de cada um dos modelos foi apresentada e analisada. As etapas de geração de dados, extração das principais características e otimização dos dados foram realizadas no software MATLAB. Uma toolbox específica deste programa foi us ada para treinar, validar e testar os 27 algoritmos diferentes e avaliar cada desempenho. Todas as etapas do trabalho foram previamente idealizadas, possibilitando seu correto desenvolvimento e execução. Os resultados mostram que o classificador "Cubic Support Vector Machine" obteve a máxima precisão entre todos os algoritmos, indicando a eficácia do método proposto para classificação. As considerações sobre os resultados foram inter pretadas, como por exemplo a explicação da performance de cada técnica, suas relações e suas justificativas

On-Line Learning and Wavelet-Based Feature Extraction Methodology for Process Monitoring using High-Dimensional Functional Data

The recent advances in information technology, such as the various automatic data acquisition systems and sensor systems, have created tremendous opportunities for collecting valuable process data. The timely processing of such data for meaningful information remains a challenge. In this research, several data mining methodology that will aid information streaming of high-dimensional functional data are developed. For on-line implementations, two weighting functions for updating support vector regression parameters were developed. The functions use parameters that can be easily set a priori with the slightest knowledge of the data involved and have provision for lower and upper bounds for the parameters. The functions are applicable to time series predictions, on-line predictions, and batch predictions. In order to apply these functions for on-line predictions, a new on-line support vector regression algorithm that uses adaptive weighting parameters was presented. The new algorithm uses varying rather than fixed regularization constant and accuracy parameter. The developed algorithm is more robust to the volume of data available for on-line training as well as to the relative position of the available data in the training sequence. The algorithm improves prediction accuracy by reducing uncertainty in using fixed values for the regression parameters. It also improves prediction accuracy by reducing uncertainty in using regression values based on some experts’ knowledge rather than on the characteristics of the incoming training data. The developed functions and algorithm were applied to feedwater flow rate data and two benchmark time series data. The results show that using adaptive regression parameters performs better than using fixed regression parameters. In order to reduce the dimension of data with several hundreds or thousands of predictors and enhance prediction accuracy, a wavelet-based feature extraction procedure called step-down thresholding procedure for identifying and extracting significant features for a single curve was developed. The procedure involves transforming the original spectral into wavelet coefficients. It is based on multiple hypothesis testing approach and it controls family-wise error rate in order to guide against selecting insignificant features without any concern about the amount of noise that may be present in the data. Therefore, the procedure is applicable for data-reduction and/or data-denoising. The procedure was compared to six other data-reduction and data-denoising methods in the literature. The developed procedure is found to consistently perform better than most of the popular methods and performs at the same level with the other methods. Many real-world data with high-dimensional explanatory variables also sometimes have multiple response variables; therefore, the selection of the fewest explanatory variables that show high sensitivity to predicting the response variable(s) and low sensitivity to the noise in the data is important for better performance and reduced computational burden. In order to select the fewest explanatory variables that can predict each of the response variables better, a two-stage wavelet-based feature extraction procedure is proposed. The first stage uses step-down procedure to extract significant features for each of the curves. Then, representative features are selected out of the extracted features for all curves using voting selection strategy. Other selection strategies such as union and intersection were also described and implemented. The essence of the first stage is to reduce the dimension of the data without any consideration for whether or not they can predict the response variables accurately. The second stage uses Bayesian decision theory approach to select some of the extracted wavelet coefficients that can predict each of the response variables accurately. The two stage procedure was implemented using near-infrared spectroscopy data and shaft misalignment data. The results show that the second stage further reduces the dimension and the prediction results are encouraging