Early Fault Detection in Induction Motors Using AdaBoost With Imbalanced Small Data and Optimized Sampling

Martin-Diaz, Ignacio; Moriñigo-Sotelo, Daniel; Duque-Pérez, Óscar; Romero-Troncoso, René de Jesús

doi:10.1109/tia.2016.2618756

Cited by 117 publications

(33 citation statements)

References 31 publications

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“…In addition to the benefit from uncertainty tracking, GDA provides another possibility to combine the data analysis tools for big data and small data together as well. Although big data has significantly enhanced the application of AI, there are still real-world situations where big data technology cannot work and models for small data are still essential (Kennedy et al, 2017;Martin-Diaz et al, 2017;Thinyane, 2017). It is well known that grey prediction models work better for small data sets while models like neural networks work better for big data sets.…”

Section: Grey Data Analysismentioning

confidence: 99%

Uncertainty and grey data analytics

Yang

Liu

Xie

2019

MAEM

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Purpose The purpose of this paper is to propose a framework for data analytics where everything is grey in nature and the associated uncertainty is considered as an essential part in data collection, profiling, imputation, analysis and decision making. Design/methodology/approach A comparative study is conducted between the available uncertainty models and the feasibility of grey systems is highlighted. Furthermore, a general framework for the integration of grey systems and grey sets into data analytics is proposed. Findings Grey systems and grey sets are useful not only for small data, but also big data as well. It is complementary to other models and can play a significant role in data analytics. Research limitations/implications The proposed framework brings a radical change in data analytics. It may bring a fundamental change in our way to deal with uncertainties. Practical implications The proposed model has the potential to avoid the mistake from a misleading data imputation. Social implications The proposed model takes the philosophy of grey systems in recognising the limitation of our knowledge which has significant implications in our way to deal with our social life and relations. Originality/value This is the first time that the whole data analytics is considered from the point of view of grey systems.

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Section: Grey Data Analysismentioning

confidence: 99%

Uncertainty and grey data analytics

Yang

Liu

Xie

2019

MAEM

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“…Ensemble learning provides a substantial classification effect by integrating weak classifiers into a strong classifier. Ensemble learning algorithms that can handle class imbalance problem include the AdaBoost (Martin-Diaz et al 2016;Sun et al 2020) and RUSBoost algorithms (Dwiyanti and Ardiyanti et al 2016).…”

Section: Introductionmentioning

confidence: 99%

L-measure evaluation metric for fake information detection models with binary class imbalance

Wang

Hsu

et al. 2020

Enterprise Information Systems

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Fake information in social media frequently causes social issues. The amount of fake information is smaller than that of real information, this leads to class imbalance. Some improved classification methods and metrics to resolve the imbalance and evaluate model performance have been proposed, respectively. However, the existing metrics for classification methods have many limitations. This paper proposes the robust metric, L-measure, that can reasonably evaluate all models with binary class imbalance with different IRs. L-measure also require less computation than the Matthews correlation coefficient. Finally, this paper demonstrates the validity of the proposed metric under different IRs with examples from UCI and Kaggle.

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“…Representam 85% do total de motores utilizados no setor e em países desenvolvidos, chegam a demandar cerca de 45% de toda energia gerada. (Martin-Diaz et al 2017) (Kowsari et al 2017) (Mirzaeva et al 2018) (Picazo-Rodenas et al 2012) Com os fatos, fica evidente a importância desta máquina, o que justifica o número de pesquisas realizadas na área. Uma grande parte dos trabalhos é voltada para a detecção de defeitos que podem ocorrer durante sua operação.…”

Section: Introductionunclassified

Parâmetros Relevantes Na Análise Termográfica De Um Motor de Indução Trifásico

Moreira

Lamim

Araújo

et al. 2019

Anais Do 14º Simpósio Brasileiro De Automação Inteligente

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The diagnosis of engine failures is a topic that is important in the industrial context. Several techniques have been developed in this sense and one of them is the thermographic analysis. This paper presents a contribution about the interference caused by measurement parameters in a thermal analysis of an induction motor. Through different tests, the main points to be verified in a thermographic camera calibration, the errors caused by the bad adjustment of the parameters and viable solutions were presented. Resumo: O diagnóstico de falhas em motores é um tópico que tem importância no contexto industrial. Várias técnicas têm sido desenvolvidas neste sentido e uma delas é a análise termográfica. Este artigo apresenta uma contribuição acerca da interferência causada por parâmetros de medição em uma análise térmica de um motor de indução. Através de diferentes testes, foram apresentados os principais pontos a serem verificados em uma aferição por câmera termográfica, os erros causados pelo mal ajuste dos parâmetros e soluções viáveis.

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Early Fault Detection in Induction Motors Using AdaBoost With Imbalanced Small Data and Optimized Sampling

Cited by 117 publications

References 31 publications

Uncertainty and grey data analytics

Uncertainty and grey data analytics

L-measure evaluation metric for fake information detection models with binary class imbalance

Parâmetros Relevantes Na Análise Termográfica De Um Motor de Indução Trifásico

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