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

Claudio Rebelo de Sá; Arvind Kumar Shekar; Hugo Ferreira; Carlos Soares

doi:10.1007/978-3-030-20055-8_14

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

Claudio Rebelo de Sá, Arvind Kumar Shekar, Hugo Ferreira, Carlos Soares

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › Academic › peer-review

1 Citation (Scopus)

103 Downloads (Pure)

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 language	English
Title of host publication	14th International Conference on Soft Computing Models in Industrial and Environmental Applications (SOCO 2019)
Editors	Héctor Quintián, José António Sáez Muñoz, Emilio Corchado, Francisco Martínez Álvarez, Alicia Troncoso Lora
Place of Publication	Cham
Publisher	Springer
Pages	142-153
Number of pages	12
ISBN (Electronic)	978-3-030-20055-8
ISBN (Print)	978-3-030-20054-1
DOIs	https://doi.org/10.1007/978-3-030-20055-8_14
Publication status	Published - 1 May 2019
Event	14th International Conference on Soft Computing Models in Industrial and Environmental Applications 2019 - Seville, Spain Duration: 13 May 2019 → 15 May 2019 Conference number: 14 http://2019.sococonference.eu/

Publication series

Name	Advances in Intelligent Systems and Computing
Publisher	Springer
Volume	950
ISSN (Print)	2194-5357
ISSN (Electronic)	2194-5365

Conference

Conference	14th International Conference on Soft Computing Models in Industrial and Environmental Applications 2019
Abbreviated title	SOCO 2019
Country/Territory	Spain
City	Seville
Period	13/05/19 → 15/05/19
Internet address	http://2019.sococonference.eu/

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10.1007/978-3-030-20055-8_14

Shekar2018buildingAccepted author version, 481 KB

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Rebelo de Sá, C., Shekar, A. K., 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) (pp. 142-153). (Advances in Intelligent Systems and Computing; Vol. 950). Springer. https://doi.org/10.1007/978-3-030-20055-8_14

Rebelo de Sá, Claudio ; Shekar, Arvind Kumar ; Ferreira, Hugo et al. / 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. Cham : Springer, 2019. pp. 142-153 (Advances in Intelligent Systems and Computing).

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title = "Building Robust Prediction Models for Defective Sensor Data Using Artificial Neural Networks",

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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booktitle = "14th International Conference on Soft Computing Models in Industrial and Environmental Applications (SOCO 2019)",

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Rebelo de Sá, C, Shekar, AK, 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). Advances in Intelligent Systems and Computing, vol. 950, Springer, Cham, 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. / Rebelo de Sá, Claudio; Shekar, Arvind Kumar; Ferreira, Hugo et al.
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. Cham: Springer, 2019. p. 142-153 (Advances in Intelligent Systems and Computing; Vol. 950).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › Academic › peer-review

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AU - Soares, Carlos

N1 - Conference code: 14

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N2 - 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.

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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Rebelo de Sá C, Shekar AK, 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). Cham: Springer. 2019. p. 142-153. (Advances in Intelligent Systems and Computing). doi: 10.1007/978-3-030-20055-8_14

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

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