Reliability-centered maintenance: analyzing failure in harvest sugarcane machine using some generalizations of the Weibull distribution

In this study we considered five generalizations of the standard Weibull distribution to describe the lifetime of two important components of harvest sugarcane machines. The harvesters considered in the analysis does the harvest of an average of 20 tons of sugarcane per hour and their malfunction may lead to major losses, therefore, an effective maintenance approach is of main interesting for cost savings. For the considered distributions, the mathematical background is presented. Maximum likelihood is used for parameter estimation. Further, different discrimination procedures were used to obtain the best fit for each component. At the end, we propose a maintenance scheduling for the components of the harvesters using predictive analysis

Recognition of dog movements using an accelerometer and artificial neural networks

A classificação dos movimentos de cães utilizando dados de acelerômetro é uma área ainda pouco explorada no Brasil, mas de grande importância para o acompanhamento da saúde e bem estar destes animais. Este trabalho propõe um método de classificação de movimentação dos cães, a partir de um acelerômetro triaxial, e utilização de três arquiteturas de redes neurais artificiais: Rede Neural Convolucional (CNN), Rede Neural Convolucional associada a Long Short Term Memory (CNN-LSTM) e ConvLSTM. A metodologia foi desenvolvida instalando um pingente contendo o acelerômetro na coleira de 8 cachorros, que coletava dados em uma frequência de 10 Hz. Para avaliar o desempenho das redes neurais foi considerado o coeficiente de Matthews, que é um indicador muito utilizado na área de bioinformática. A arquitetura com melhor desempenho foi a ConvLSTM, que apresentou um coeficiente de Matthews de 0,79 no conjunto de teste.Classification of dogs movements by using data collected from accelerometers is an area little explored in Brazil, but this is of great importance to monitor health and well-being of these animals. This work proposes a method to classify the movement of dogs using a triaxial accelerometer, and the use of three artificial neural network architectures: Convolutional Neural Network (CNN), Convolutional Neural Network associated with Long Short Term Memory ( CNN-LSTM) and ConvLSTM. The methodology was developed by installing a pendant that contains the accelerometer on the collar of 8 dogs, and it presents data collected at a frequency of 10 Hz. To evaluate the neural network performance the Matthews coefficient was considered, which is an widely used indicator in the area of bioinformatics. The best performing architecture was ConvLSTM, which had a Matthews coefficient of 0.79 on the test se

Reliability-Centered Maintenance: Analyzing Failure in Harvest Sugarcane Machine Using Some Generalizations of the Weibull Distribution

We considered five generalizations of the standard Weibull distribution to describe the lifetime of two important components of sugarcane harvesting machines. The harvesters considered in the analysis harvest an average of 20 tons of sugarcane per hour and their malfunction may lead to major losses; therefore, an effective maintenance approach is of main interest for cost savings. For the considered distributions, mathematical background is presented. Maximum likelihood is used for parameter estimation. Further, different discrimination procedures were used to obtain the best fit for each component. At the end, we propose a maintenance scheduling for the components of the harvesters using predictive analysis