Fuzzy expert system for metal-oxide surge arrester condition monitoring

Dobrić, Goran; Žarković, Mileta

doi:10.1007/s00202-020-01061-z

Cited by 6 publications

(6 citation statements)

References 23 publications

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“…This approach has the potential to increase the volume of signals available during cluster-centric signal generation. Integration of newer clustering methods, such as OPTICS [ 36 ] and Hierarchical DBSCAN [ 37 ], LSTM [ 38 ], and a fuzzy logic expert system [ 39 , 40 ] could be explored to improve the versatility of the current workflow. …”

Section: Discussionmentioning

confidence: 99%

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Deep learning and predictive modelling for generating normalised muscle function parameters from signal images of mandibular electromyography

Farook,

Haq,

Ramees

et al. 2024

Med Biol Eng Comput

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Challenges arise in accessing archived signal outputs due to proprietary software limitations. There is a notable lack of exploration in open-source mandibular EMG signal conversion for continuous access and analysis, hindering tasks such as pattern recognition and predictive modelling for temporomandibular joint complex function. To Develop a workflow to extract normalised signal parameters from images of mandibular muscle EMG and identify optimal clustering methods for quantifying signal intensity and activity durations. A workflow utilising OpenCV, variational encoders and Neurokit2 generated and augmented 866 unique EMG signals from jaw movement exercises. k-means, GMM and DBSCAN were employed for normalisation and cluster-centric signal processing. The workflow was validated with data collected from 66 participants, measuring temporalis, masseter and digastric muscles. DBSCAN (0.35 to 0.54) and GMM (0.09 to 0.24) exhibited lower silhouette scores for mouth opening, anterior protrusion and lateral excursions, while K-means performed best (0.10 to 0.11) for temporalis and masseter muscles during chewing activities. The current study successfully developed a deep learning workflow capable of extracting normalised signal data from EMG images and generating quantifiable parameters for muscle activity duration and general functional intensity. Graphical Abstract

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Section: Discussionmentioning

confidence: 99%

“…3. Integration of newer clustering methods, such as OPTICS [36] and Hierarchical DBSCAN [37], LSTM [38], and a fuzzy logic expert system [39,40] could be explored to improve the versatility of the current workflow.…”

Section: Future Recommendationsmentioning

confidence: 99%

Deep learning and predictive modelling for generating normalised muscle function parameters from signal images of mandibular electromyography

Farook,

Haq,

Ramees

et al. 2024

Med Biol Eng Comput

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“…Na literatura há diversas técnicas de monitoramento e diagnóstico da degradação dos para-raios de ZnO, sendo a maioria delas baseadas na medição da temperatura e extração da corrente resistiva (IEC 60099-5 2013;Lima Júnior 2014;Lira et al 2018;Dobrić & Žarković 2020). Outros estudos foram propostos a fim de desenvolver técnicas inteligentes de diagnóstico de para-raios, em que grande parte delas utilizam inteligência artificial para compor um classificador de estados, utilizando outros indicadores, tais como relações derivadas do conteúdo harmônica da corrente (Lira & Costa 2013;Khodsuz & Mirzaie 2015;Dobrić & Žarković 2020). Adicionalmente, trabalhos têm sido publicados relatando a eficácia da terceira componente harmônica da corrente para monitoramento e diagnóstico de para-raios, com foco na aplicação em campo (Shirakawa et al 1988;Lima Júnior 2014;Metwally, Eladawy & Feilat 2017;Dobrić & Žarković 2020).…”

Section: Introductionunclassified

“…Outros estudos foram propostos a fim de desenvolver técnicas inteligentes de diagnóstico de para-raios, em que grande parte delas utilizam inteligência artificial para compor um classificador de estados, utilizando outros indicadores, tais como relações derivadas do conteúdo harmônica da corrente (Lira & Costa 2013;Khodsuz & Mirzaie 2015;Dobrić & Žarković 2020). Adicionalmente, trabalhos têm sido publicados relatando a eficácia da terceira componente harmônica da corrente para monitoramento e diagnóstico de para-raios, com foco na aplicação em campo (Shirakawa et al 1988;Lima Júnior 2014;Metwally, Eladawy & Feilat 2017;Dobrić & Žarković 2020).…”

Section: Introductionunclassified

Metodologia para Estimar a Vida Útil de Para-raios de Óxido de Zinco Baseada na Aplicação do Sistema Neuro-Fuzzy Adaptativo

Vandilson R. N. Barbosa,

Edson G. Costa,

George R. S. Lira

et al. 2022

Proceedings IX Simpósio Brasileiro De Sistemas Elétricos

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This work presents a methodology to be used to estimate the Metal Oxide Surge Arresters' (MOSAs') useful life, based on the prediction model based on the Adaptive Neuro-Fuzzy Inference System (ANFIS). For this purpose, a database consisting of MOSA's total leakage current was used. Thus, it was possible to build a time series composed of values of the third harmonic component of the leakage current. This component is one of the indicators of the MOSAs' degradation level most used in the surge arresters monitoring. Subsequently, the forecasting models based on artificial intelligence (ANFIS and Support Vector Regression -SVR) were implemented, evaluated, and compared. During implementations, the ANFIS model was tested with three distinct membership functions: Gaussian, Generalized Bell-Shaped, and Pi-Shaped; and the SVR model was tested with three different kernel functions: Gaussian, Linear, and Polynomial. The performance of each implemented model was evaluated using the determination coefficient in the training phase of the models and the mean absolute percentage error, in the validation phase. Considering the results obtained by the ANFIS e SVR models, it was found that the forecasts made using the ANFIS model with Gaussian membership function were the most accurate forecasts. Thus, the proposed methodology, which applies the ANFIS model with Gaussian function, was used to estimate the MOSA's useful life.Resumo: Este trabalho apresenta uma metodologia para ser utilizada na estimação da vida útil de pararaios de Óxido de Zinco (ZnO), utilizando um modelo de previsão baseado no Sistema de Inferência Neuro-Fuzzy Adaptativo (ANFIS, do inglês Adaptive Neuro-Fuzzy Inference System). Para tanto, um banco de dados foi utilizado, o qual foi constituído por sinais da corrente de fuga total de para-raios. Assim, foi possível construir uma série temporal composta por valores da terceira componente harmônica da corrente de fuga. Essa componente é um dos indicadores do nível de degradação de para-raios mais empregados no monitoramento desses equipamentos. Em seguida, modelos de previsão baseados em inteligência artificial, os modelos ANFIS e Regressão por Vetores de Suporte (SVR, do inglês Support Vector Regression), foram implementados, avaliados e comparados, a fim de identificar o modelo que apresenta o melhor desempenho. Durante as implementações, o modelo ANFIS foi testado com três funções diferentes de pertinência: Gaussiana, Sino Generalizada e Pi; e o modelo SVR foi testado com três funções diferentes de kernel: Gaussiana, Linear e Polinomial. A avaliação do desempenho de cada um dos modelos implementados foi realizada mediante o emprego do coeficiente de determinação, na fase de treinamento, e a utilização do erro percentual absoluto médio, na fase de validação. Considerando os resultados obtidos pelos modelos ANFIS e SVR, constatou-se que as previsões realizadas por meio do modelo ANFIS com função de pertinência Gaussiana apresenta as previsões mais exatas. Dessa forma, a metodologia proposta, que aplica o modelo ...

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“…They showed that the caution threshold can be used to alarm about abnormalities and estimate the abnormal probability. Fuzzy expert system is also applied in MOSA monitoring procedure [18].…”

Section: Introductionmentioning

confidence: 99%

Machine Learning MOSA Monitoring System

Manjunath,

Vrtagić,

Dogan

et al. 2021

I2M

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This research paper deals with the problem of Metal-Oxide Surge Arrester (MOSA) condition monitoring and a new methodology in surge arrester monitoring and diagnostics is presented. A machine learning algorithm (back propagation regression) is used to estimate the non-linearity coefficient of the surge arrester, based on operating voltage and leakage current of the arrester. Using a simulated system, this research investigates the possibility of application and efficiency of machine learning. It is shown that the applied learning algorithm results are competitive with the model results parameters calculated as R2 = 0.999 and mean absolute real error computed as 0.005 which has shown that the proposed model can be used for MOSA monitoring and diagnostic purposes.

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Fuzzy expert system for metal-oxide surge arrester condition monitoring

Cited by 6 publications

References 23 publications

Deep learning and predictive modelling for generating normalised muscle function parameters from signal images of mandibular electromyography

Deep learning and predictive modelling for generating normalised muscle function parameters from signal images of mandibular electromyography

Metodologia para Estimar a Vida Útil de Para-raios de Óxido de Zinco Baseada na Aplicação do Sistema Neuro-Fuzzy Adaptativo

Machine Learning MOSA Monitoring System

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