The design of multiple linear regression models using a genetic algorithm to diagnose initial short-circuit faults in 3-phase induction motors

Júnior, Arismar Morais Gonçalves; Silva, Valceres Vieira Rocha e; Baccarini, Lane Maria Rabelo; Mendes, Lívia F. S.

doi:10.1016/j.asoc.2017.11.015

Cited by 19 publications

(5 citation statements)

References 29 publications

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“…To estimate the coefficients of MLR models, the least squares method given for the vector sample β in Equation () is generally used. Thus, MLR models can be developed according to the existence of errors 45

β = {((), X^{'} bold-italicX)}^{- 1} {bold-italicX}^{'} Y .

…”

Section: Methodsmentioning

confidence: 99%

Prediction of electricity energy consumption including COVID‐19 precautions using the hybrid MLR‐FFANN optimized with the stochastic fractal search with fitness distance balance algorithm

Dalcalı

Özbay

Duman

2022

Concurrency and Computation

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The increase in energy consumption is affected by the developments in technology as well as the global population growth. Increasing energy consumption makes it difficult to ensure electrical energy supply security. Meeting the energy demand can be achieved with the right planning. Proper planning is critical for both economical use of resources and low cost for the end consumer. On the other hand, erroneous estimation of demand may cause waste of resources and energy crisis. Accurate estimation is possible by accurately modeling the factors affecting electricity consumption. Apart from known factors such as seasonal conditions, days of the week and hours, modeling in extreme events such as pandemics that affect all our behaviors increases the success in modeling the future projection. This ensures that the security of electrical energy supply is carried out effectively with limited resources. For this purpose, in this study, a hybrid multiple linear regression-feedforward artificial neural network (MLR-FFANN) based algorithm model was proposed, taking into account the estimated impact of the COVID-19 pandemic on the energy consumption values of Bursa, an industrial city in Turkey. The aim of the hybrid MLR-FFANN approach was to simultaneously optimize the β polynomial for multiple linear regression and the weight and bias coefficients for the forward propagation neural network using the adaptive guided differential evolution, equilibrium optimizer, slime mold algorithm, and stochastic fractal search with fitness distance balance (SFSFDB) optimization algorithms. The success of the model whose parameters were optimized using the optimization algorithms was determined according to mean absolute error, mean absolute percentage error, and root mean square error evaluation criteria and statistical analysis of these results. According to the results of the analysis, the MLR-FFANN approach whose parameters were optimized with the SFSFDB algorithm was more successful in the training of the dataset containing the COVID-19 precautions.

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β = {((), X^{'} bold-italicX)}^{- 1} {bold-italicX}^{'} Y .

…”

Section: Methodsmentioning

confidence: 99%

Prediction of electricity energy consumption including COVID‐19 precautions using the hybrid MLR‐FFANN optimized with the stochastic fractal search with fitness distance balance algorithm

Dalcalı

Özbay

Duman

2022

Concurrency and Computation

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“…For more complex tasks, multiple RFs may be needed. This is because the number of possible combinations for a given problem increases exponentially as the data set size grows [51]. Consider a training data set {x 1 , x 2 , x 3 , ..., x n } ∈ R Lxn , n is the number of samples, and L is the number of characteristics.…”

Section: Random Forestmentioning

confidence: 99%

Fire resistance evaluation through synthetic fire tests and generative adversarial networks

Çiftçioğlu,

Naser

2024

Front. Struct. Civ. Eng.

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This paper introduces a machine learning approach to address the challenge of limited data resulting from costly and time-consuming fire experiments by enlarging small fire test data sets and predicting the fire resistance of reinforced concrete columns. Our approach begins by creating deep learning models, namely generative adversarial networks and variational autoencoders, to learn the spatial distribution of real fire tests. We then use these models to generate synthetic tabular samples that closely resemble realistic fire resistance values for reinforced concrete columns. The generated data are employed to train state-of-the-art machine learning techniques, including Extreme Gradient Boost, Light Gradient Boosting Machine, Categorical Boosting Algorithm, Support Vector Regression, Random Forest, Decision Tree, Multiple Linear Regression, Polynomial Regression, Support Vector Machine, Kernel Support Vector Machine, Naive Bayes, and K-Nearest Neighbors, which can predict the fire resistance of the columns through regression and classification. Machine learning analyses achieved highly accurate predictions of fire resistance values, outperforming traditional models that relied solely on limited experimental data. Our study highlights the potential for using machine learning and deep learning analyses to revolutionize the field of structural engineering by improving the accuracy and efficiency of fire resistance evaluations while reducing the reliance on costly and time-consuming experiments.

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“…Other works such as the one presented by Pramesti et al [84] involve the identification of stator failures in induction motors using the multinomial logistic regression analysis and the Wavelet Transform (WT). Júnior et al [85] use a multiple linear regression modeling technique along with the analysis of variance and the genetic algorithm optimization to obtain classification models to diagnose three-phase induction motors under normal and short-circuit conditions. The method presents percentages of hits greater than 95% in the diagnosis of the normal and incipient short-circuit fault condition, even at different motor load levels.…”

Section: Hybrid Techniquesmentioning

confidence: 99%

Predictive Data Mining Techniques for Fault Diagnosis of Electric Equipment: A Review

et al. 2020

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Data mining is a technological and scientific field that, over the years, has been gaining more importance in many areas, attracting scientists, developers, and researchers around the world. The reason for this enthusiasm derives from the remarkable benefits of its usefulness, such as the exploitation of large databases and the use of the information extracted from them in an intelligent way through the analysis and discovery of knowledge. This document provides a review of the predictive data mining techniques used for the diagnosis and detection of faults in electric equipment, which constitutes the pillar of any industrialized country. Starting from the year 2000 to the present, a revision of the methods used in the tasks of classification and regression for the diagnosis of electric equipment is carried out. Current research on data mining techniques is also listed and discussed according to the results obtained by different authors.

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The design of multiple linear regression models using a genetic algorithm to diagnose initial short-circuit faults in 3-phase induction motors

Cited by 19 publications

References 29 publications

Prediction of electricity energy consumption including COVID‐19 precautions using the hybrid MLR‐FFANN optimized with the stochastic fractal search with fitness distance balance algorithm

Prediction of electricity energy consumption including COVID‐19 precautions using the hybrid MLR‐FFANN optimized with the stochastic fractal search with fitness distance balance algorithm

Fire resistance evaluation through synthetic fire tests and generative adversarial networks

Predictive Data Mining Techniques for Fault Diagnosis of Electric Equipment: A Review

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