Building Robust Prediction Models for Defective Sensor Data Using Artificial Neural Networks

Claudio Frederico Pinho Rebelo de Sá, Arvind Kumar, Hugo Ferreira, Carlos Soares

Research output: Chapter in Book/Report/Conference proceedingConference contributionAcademicpeer-review

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Abstract

Sensors are susceptible to failure when exposed to extreme conditions over long periods of time. Besides they can be affected by noise or electrical interference. Models (Machine Learning or others) obtained from these faulty and noisy sensors may be less reliable. In this paper, we propose a data augmentation approach for making neural networks more robust to missing and faulty sensor data. This approach is shown to be effective in a real life industrial application that uses data of various sensors to predict the wear of an automotive fuel-system component. Empirical results show that the proposed approach leads to more robust neural network in this particular application than existing methods.
Original languageEnglish
Title of host publication14th International Conference on Soft Computing Models in Industrial and Environmental Applications (SOCO 2019)
EditorsHéctor Quintián, José António Sáez Muñoz, Emilio Corchado, Francisco Martínez Álvarez, Alicia Troncoso Lora
Pages142-153
Number of pages12
Volume950
ISBN (Electronic)978-3-030-20055-8
DOIs
Publication statusE-pub ahead of print/First online - 1 May 2019
Event14th International Conference on Soft Computing Models in Industrial and Environmental Applications 2019 - Seville, Spain
Duration: 13 May 201915 May 2019
Conference number: 14
http://2019.sococonference.eu/

Publication series

NameAdvances in Intelligent Systems and Computing
Volume950
ISSN (Print)2194-5357
ISSN (Electronic)2194-5365

Conference

Conference14th International Conference on Soft Computing Models in Industrial and Environmental Applications 2019
Abbreviated titleSOCO 2019
CountrySpain
CitySeville
Period13/05/1915/05/19
Internet address

Fingerprint

Neural networks
Sensors
Automotive fuels
Fuel systems
Industrial applications
Learning systems
Wear of materials

Cite this

Pinho Rebelo de Sá, C. F., Kumar, A., Ferreira, H., & Soares, C. (2019). Building Robust Prediction Models for Defective Sensor Data Using Artificial Neural Networks. In H. Quintián, J. A. Sáez Muñoz, E. Corchado, F. Martínez Álvarez, & A. Troncoso Lora (Eds.), 14th International Conference on Soft Computing Models in Industrial and Environmental Applications (SOCO 2019) (Vol. 950, pp. 142-153). (Advances in Intelligent Systems and Computing; Vol. 950). https://doi.org/10.1007/978-3-030-20055-8_14
Pinho Rebelo de Sá, Claudio Frederico ; Kumar, Arvind ; Ferreira, Hugo ; Soares, Carlos. / Building Robust Prediction Models for Defective Sensor Data Using Artificial Neural Networks. 14th International Conference on Soft Computing Models in Industrial and Environmental Applications (SOCO 2019). editor / Héctor Quintián ; José António Sáez Muñoz ; Emilio Corchado ; Francisco Martínez Álvarez ; Alicia Troncoso Lora. Vol. 950 2019. pp. 142-153 (Advances in Intelligent Systems and Computing).
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abstract = "Sensors are susceptible to failure when exposed to extreme conditions over long periods of time. Besides they can be affected by noise or electrical interference. Models (Machine Learning or others) obtained from these faulty and noisy sensors may be less reliable. In this paper, we propose a data augmentation approach for making neural networks more robust to missing and faulty sensor data. This approach is shown to be effective in a real life industrial application that uses data of various sensors to predict the wear of an automotive fuel-system component. Empirical results show that the proposed approach leads to more robust neural network in this particular application than existing methods.",
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Pinho Rebelo de Sá, CF, Kumar, A, Ferreira, H & Soares, C 2019, Building Robust Prediction Models for Defective Sensor Data Using Artificial Neural Networks. in H Quintián, JA Sáez Muñoz, E Corchado, F Martínez Álvarez & A Troncoso Lora (eds), 14th International Conference on Soft Computing Models in Industrial and Environmental Applications (SOCO 2019). vol. 950, Advances in Intelligent Systems and Computing, vol. 950, pp. 142-153, 14th International Conference on Soft Computing Models in Industrial and Environmental Applications 2019, Seville, Spain, 13/05/19. https://doi.org/10.1007/978-3-030-20055-8_14

Building Robust Prediction Models for Defective Sensor Data Using Artificial Neural Networks. / Pinho Rebelo de Sá, Claudio Frederico; Kumar, Arvind; Ferreira, Hugo; Soares, Carlos.

14th International Conference on Soft Computing Models in Industrial and Environmental Applications (SOCO 2019). ed. / Héctor Quintián; José António Sáez Muñoz; Emilio Corchado; Francisco Martínez Álvarez; Alicia Troncoso Lora. Vol. 950 2019. p. 142-153 (Advances in Intelligent Systems and Computing; Vol. 950).

Research output: Chapter in Book/Report/Conference proceedingConference contributionAcademicpeer-review

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AB - Sensors are susceptible to failure when exposed to extreme conditions over long periods of time. Besides they can be affected by noise or electrical interference. Models (Machine Learning or others) obtained from these faulty and noisy sensors may be less reliable. In this paper, we propose a data augmentation approach for making neural networks more robust to missing and faulty sensor data. This approach is shown to be effective in a real life industrial application that uses data of various sensors to predict the wear of an automotive fuel-system component. Empirical results show that the proposed approach leads to more robust neural network in this particular application than existing methods.

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Pinho Rebelo de Sá CF, Kumar A, Ferreira H, Soares C. Building Robust Prediction Models for Defective Sensor Data Using Artificial Neural Networks. In Quintián H, Sáez Muñoz JA, Corchado E, Martínez Álvarez F, Troncoso Lora A, editors, 14th International Conference on Soft Computing Models in Industrial and Environmental Applications (SOCO 2019). Vol. 950. 2019. p. 142-153. (Advances in Intelligent Systems and Computing). https://doi.org/10.1007/978-3-030-20055-8_14