The Discrimination Between Water and Oil Using Ultrasonic Sensor

Tokitou, Ken-Ichi; Shida, Katunori

doi:10.1541/ieejsmas.124.415

Cited by 4 publications

(4 citation statements)

References 4 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…As previously mentioned, some research involving acoustic and ultrasonic analysis in the detection of oil in transformers has been done in the last few decades [10][11][12]15,16].…”

Section: Related Workmentioning

confidence: 99%

“…In the article by Tokitou and Shida [10], a detection system to discriminate water in oil is presented. The detection method adopts the difference in the propagation time of an ultrasonic wave in water and oil, for each characteristic temperature.…”

Section: Related Workmentioning

confidence: 99%

“…Some studies address acoustic and ultrasonic analysis as a predictive technique in the transformers monitoring for the detection of moisture in transformers oil [10][11][12]. Normally, water is hardly dissolved in oil due to the polarity of the molecules and the hydrophobic characteristic of the oil.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Ferroelectret-based Hydrophone Employed in Oil Identification—A Machine Learning Approach

Luna

Palitó

Assagra

et al. 2020

Sensors

View full text Add to dashboard Cite

This work focuses on acoustic analysis as a way of discriminating mineral oil, providing a robust technique, immune to electromagnetic noise, and in some cases, depending on the applied sensor, a low-cost technique. Thus, we propose a new method for the diagnosis of the quality of mineral oil used in electrical transformers, integrating a ferroelectric-based hydrophone and an acoustic transducer. Our classification solution is based on a supervised machine learning technique applied to the signals generated by an in-home built hydrophone. A total of three statistical datasets entries were collected during the acoustic experiments on four types of oils. The first, the second, and third datasets contain 180, 240, and 420 entries, respectively. Eighty-four features were considered from each dataset to apply to two classification approaches. The first classification approach is able to distinguish the oils from the four possible classes with a classification error less than 2%, while the second approach is able to successfully classify the oils without errors (e.g., with a score of 100%).

show abstract

“…As previously mentioned, some research involving acoustic and ultrasonic analysis in the detection of oil in transformers has been done in the last few decades [10][11][12]15,16].…”

Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

See 1 more Smart Citation

Ferroelectret-based Hydrophone Employed in Oil Identification—A Machine Learning Approach

Luna

Palitó

Assagra

et al. 2020

Sensors

View full text Add to dashboard Cite

show abstract

“…Um tópico destacado nessa área é o método baseado na propagação de ondas acústicas, comumente conhecido como método de emissão acústica (AE). Pesquisas recentes revelam a utilização de sensores acústicos e ultrassônicos para caracterização de líquidos conforme relatado na literatura (MENG;JAWORSKI;WHITE, 2006;FRANCO;BUIOCHI, 2012;CHAUDHURI et al, 2014;XIAO, 2015;TOKITOU;SHIDA, 2004;TYURYUMINA;BATRAK;SEKACKIY, 2017). Meng, Jaworski e White (2006) apresentaram um estudo experimental realizado para caracterizar misturas heterogêneas (emulsões) de petróleo e água, utilizando transdutores ultrassônicos.…”

Section: Motivaçãounclassified

Metodologia acústica para análise de óleo de transformador por sensores piezoelétricos

Palitó¹

View full text Add to dashboard Cite

A Deus por me permitir chegar até aqui e ser a fonte da minha força e coragem em muitos momentos. E a nossa senhora pela fiel interseção. A meu orientador Prof. Ruy Alberto Correa Altafim, e ao meu coorientador Prof. Ruy Alberto Pisani Altafim, obrigada por acreditarem em mim e por todo o conhecimento transmitido durante estes 4 anos de pesquisa. Ao Dr. Yuri Andrey Assagra Olivato pelo companheirismo, amizade e ajuda durante o desenvolvimento desta tese. Ao grande amigo e técnico Sr. Rui Bertho agradeço a sua ajuda e carinho em muitos momentos, desde a fabricação das peças dos meus experimentos ao ombro amigo para chorar. Ao Prof. Adilton Carneiro pelo acolhimento, discussões e conhecimentos transmitidos no Laboratório do Grupo de Inovação em Instrumentação Médica e Ultrassom (GIIMUS) e ao técnico Agnelo Bastos pelo suporte durante os experimentos. Ao Prof. João do Carmo pelo incentivo e ajuda com os artigos. A empresa Potencial Manutenção e Comércio de Equipamentos Ltda pela parceria, cedendo as amostras e fornecendo os laudos técnicos de óleos de transformadores, para desenvolvimento desta pesquisa. A Universidade Federal da Paraíba (UFPB) por me acolher no Laboratório de Medidas e Instrumentação (LMI), onde eu realizei alguns experimentos. Ao Grupo de Alta Tensão e Medidas (Gatm) pelos momentos de descontração e ajuda no Laboratório de Alta Tensão e Medidas (LATM). Ao técnico José Roberto Bertho do Grupo de Polímeros "Prof. Bernhard Gross" do Instituto de Física de São Carlos (IFSC) pelas metalizações dos sensores piezoelétricos.

show abstract

Sensitivity comparison of impedance sensors with different coil arrangements

Zhang

Zeng

et al. 2020

IOP Conf. Ser.: Earth Environ. Sci.

View full text Add to dashboard Cite

Two inductance-capacitance dual mode sensors based on different arrangement of dual coil and microchannel for oil multi-contaminant detection are proposed. The inductance detection model of metal particles and capacitance detection model of non-metal particles are constructed theoretically. The experimental results show that the two dual mode sensors both can realize inductance detection of iron and copper particles and capacitance detection of water droplets and air bubbles in oil. The detection sensitivity can be effectively improved by placing the microchannel on the edge of coil inner hole. The sensor of dual coils perpendicular to microchannel has more sensitive in inductance detection and the sensor of dual coils parallel to microchannel has more sensitive in capacitance detection. This research is helpful to improve the design and sensitivity of oil multi-contaminant detection sensor, and is of great significance to the realization of health monitoring and fault diagnosis of machinery.

show abstract

The Discrimination Between Water and Oil Using Ultrasonic Sensor

Cited by 4 publications

References 4 publications

Ferroelectret-based Hydrophone Employed in Oil Identification—A Machine Learning Approach

Ferroelectret-based Hydrophone Employed in Oil Identification—A Machine Learning Approach

Metodologia acústica para análise de óleo de transformador por sensores piezoelétricos

Sensitivity comparison of impedance sensors with different coil arrangements

Contact Info

Product

Resources

About