Navegando por Assunto "Feature selection"

Agora exibindo 1 - 3 de 3

Acesso aberto (Open Access)
Classificação fonética utilizando Boosting e SVM
(Universidade Federal do Pará, 2006-02-17) TEIXEIRA JÚNIOR, Talisman Cláudio de Queiroz; PELAES, Evaldo Gonçalves; http://lattes.cnpq.br/0255430734381362; KLAUTAU JÚNIOR, Aldebaro Barreto da Rocha; http://lattes.cnpq.br/1596629769697284
With the aim of setting up a Automatic Speech Recognition (ASR) system, a task named Phonetic Classification can be used. That task consists in, from a speech sample, deciding which phoneme was pronounced by a speaker. To ease the classification task and to enhance the most marked characteristics of the phonemes, the speech samples are usually pre-processed by a front-end. A front-end, as a general rule, extracts a set of features to each speech sample. After that, these features are inserted in a classification algorithm, that (already properly trained) will try to decide which phoneme was pronounced. There is a rule of thumb which says that the more features the system uses, the smaller the classification error rate will be. The disadvantage to that is the larger computational cost. Feature Selection task aims to show which are the most relevant (or more used) features in a classification task. Therefore, it is possible to discover which are the redundant features, that make little (or no) contribution to the classification task. The aim of this work is to apply SVM classificator in Phonetic Classification task, using TIMIT database, and discover the most relevant features in this classification using Boosting approach to implement Feature Selection.
Acesso aberto (Open Access)
Desenvolvimento de sistema de diagnóstico de falhas em roletes de transportadores de correia
(Universidade Federal do Pará, 2024-03-28) SOARES, João Lucas Lobato; MESQUITA, Alexandre Luiz Amarante; http://lattes.cnpq.br/3605920981600245; https://orcid.org/0000-0001-5605-8381
Belt conveyors are essential equipment in mining industry and require constant monitoring to maintain good reliability. In order to support the belt and the material being conveyed, rollers are components that constantly fail during operation, in which they present faults in bearings and surface wear in the shell as the most common failure modes. Thus, monitoring based on predictive maintenance is essential, and machine learning techniques can be used as an alternative for detecting equipment failures. In diagnostics using machine learning, the feature selection step is important to avoid loss of accuracy in the classification of the equipment's condition. The present study analyzes the performance of the decision tree algorithm and Analysis of Variance (ANOVA) as alternative methods for dimensionality reduction. Initially, the vibration signals were collected on the rollers of a belt conveyor bench and the Wavelet Packet Decomposition (WPD) was applied to the signals to obtain the energy ranges, which were used as features for classification. After the determination of the best features, two approaches were analyzed for the selection of features: one with the application of the method without dimensionality reduction and the other with the application of the decision tree. In addition, different classification algorithms were used: Support Vector Machine (SVM), k-Nearest Neighbor (kNN) and Artificial Neural Network (ANN). As a result, it was found a superior performance of diagnostic accuracy in all techniques with a reduction in the dimensionality of the characteristics selected by the decision tree. In addition, SVM, kNN and ANN showed increases in accuracy ranging among the fault diagnosis models approached.
Acesso aberto (Open Access)
Proposta de metodologia para diagnóstico de falha em rolamentos de baixa velocidade
(Universidade Federal do Pará, 2024-04-19) COSTA, Thiago Barroso; MESQUITA, Alexandre Luiz Amarante; http://lattes.cnpq.br/3605920981600245; https://orcid.org/0000-0001-5605-8381
Monitoring low-speed bearings with vibration analysis is more challenging due to the low energy level of the vibration signal that carries the failure data, making it more susceptible to interference from other sources, impairing the interpretation of information. Thus, an alternative is to calculate signal predictors that may be sensitive to pattern changes relative to failure onset and progression. Hence, the present work extracted different types of features, among them two nonlinear features and eleven extracted from the signal in the time domain. Those features were ranked and selected based on their sensibility to class differentiation, which was estimated using the t-Welch statistic value. Among them is the Largest Lyapunov Exponent, which, in this work, had a modification in one of its calculation steps, improving its sensitivity in some cases. In addition, the influence of the vibration signal window size on the class separability of the indicators was evaluated (which is a scarce content in low-speed bearing monitoring literature). After feature selection, the data were subjected to a linear transformation through PCA (Principal Component Analysis), aiming to reduce the data dimensionality to three dimensions and to minimize the redundancy effects of highly correlated features. In sequence, the data represented in the space of principal components were projected on a Hotelling T2 statistic control chart. The chart allowed the detection and rejection of potential outliers, which consisted of points above a limit line estimated based on F statistic distribution. Finally, binary and multiclass Support Vector Machine classification models were trained with experimental data acquired from normal conditions and three levels of incipient fault in bearing. The models performed well, mainly the binary model with test data obtained from belt conveyor pulley bearings in industrial operation.