Preprocessing of Massive Flight Data Based on Noise and Dimension Reduction

Qingshan Xu; Jie Chen; Boying Wu

doi:10.1109/ICCC51575.2020.9345080

Preprocessing of Massive Flight Data Based on Noise and Dimension Reduction

Qingshan Xu, Jie Chen, Boying Wu

School of Marine Science and Technology

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

2 Scopus citations

Abstract

The integrated modular avionics system is an important part of modern aircraft. It will generate massive and diverse types of flight data during its operation. When we want to extract useful information from flight data for aircraft system fault diagnosis and life prediction, the noise and data redundancy are the first problem to confront and it will affect the selection of feature parameters. For this reason, this article chooses the wavelet threshold de-noising method to reduce the noise of raw data, and then uses the principal component analysis method to reduce the dimensionality. The results illustrate that the effects of de-noising and dimensionality reduction are effective.

Original language	English
Title of host publication	2020 IEEE 6th International Conference on Computer and Communications, ICCC 2020
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	1706-1710
Number of pages	5
ISBN (Electronic)	9781728186351
DOIs	https://doi.org/10.1109/ICCC51575.2020.9345080
State	Published - 11 Dec 2020
Event	6th IEEE International Conference on Computer and Communications, ICCC 2020 - Chengdu, China Duration: 11 Dec 2020 → 14 Dec 2020

Publication series

Name	2020 IEEE 6th International Conference on Computer and Communications, ICCC 2020

Conference

Conference	6th IEEE International Conference on Computer and Communications, ICCC 2020
Country/Territory	China
City	Chengdu
Period	11/12/20 → 14/12/20

Keywords

Flight data
Integrated modular avionics system (IMA)
Principal component analysis (PCA)
Wavelet threshold de-noising

Access to Document

10.1109/ICCC51575.2020.9345080

Cite this

Xu, Q., Chen, J., & Wu, B. (2020). Preprocessing of Massive Flight Data Based on Noise and Dimension Reduction. In 2020 IEEE 6th International Conference on Computer and Communications, ICCC 2020 (pp. 1706-1710). Article 9345080 (2020 IEEE 6th International Conference on Computer and Communications, ICCC 2020). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ICCC51575.2020.9345080

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title = "Preprocessing of Massive Flight Data Based on Noise and Dimension Reduction",

abstract = "The integrated modular avionics system is an important part of modern aircraft. It will generate massive and diverse types of flight data during its operation. When we want to extract useful information from flight data for aircraft system fault diagnosis and life prediction, the noise and data redundancy are the first problem to confront and it will affect the selection of feature parameters. For this reason, this article chooses the wavelet threshold de-noising method to reduce the noise of raw data, and then uses the principal component analysis method to reduce the dimensionality. The results illustrate that the effects of de-noising and dimensionality reduction are effective.",

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author = "Qingshan Xu and Jie Chen and Boying Wu",

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Xu, Q, Chen, J & Wu, B 2020, Preprocessing of Massive Flight Data Based on Noise and Dimension Reduction. in 2020 IEEE 6th International Conference on Computer and Communications, ICCC 2020., 9345080, 2020 IEEE 6th International Conference on Computer and Communications, ICCC 2020, Institute of Electrical and Electronics Engineers Inc., pp. 1706-1710, 6th IEEE International Conference on Computer and Communications, ICCC 2020, Chengdu, China, 11/12/20. https://doi.org/10.1109/ICCC51575.2020.9345080

Preprocessing of Massive Flight Data Based on Noise and Dimension Reduction. / Xu, Qingshan; Chen, Jie; Wu, Boying.
2020 IEEE 6th International Conference on Computer and Communications, ICCC 2020. Institute of Electrical and Electronics Engineers Inc., 2020. p. 1706-1710 9345080 (2020 IEEE 6th International Conference on Computer and Communications, ICCC 2020).

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

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N2 - The integrated modular avionics system is an important part of modern aircraft. It will generate massive and diverse types of flight data during its operation. When we want to extract useful information from flight data for aircraft system fault diagnosis and life prediction, the noise and data redundancy are the first problem to confront and it will affect the selection of feature parameters. For this reason, this article chooses the wavelet threshold de-noising method to reduce the noise of raw data, and then uses the principal component analysis method to reduce the dimensionality. The results illustrate that the effects of de-noising and dimensionality reduction are effective.

AB - The integrated modular avionics system is an important part of modern aircraft. It will generate massive and diverse types of flight data during its operation. When we want to extract useful information from flight data for aircraft system fault diagnosis and life prediction, the noise and data redundancy are the first problem to confront and it will affect the selection of feature parameters. For this reason, this article chooses the wavelet threshold de-noising method to reduce the noise of raw data, and then uses the principal component analysis method to reduce the dimensionality. The results illustrate that the effects of de-noising and dimensionality reduction are effective.

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Xu Q, Chen J, Wu B. Preprocessing of Massive Flight Data Based on Noise and Dimension Reduction. In 2020 IEEE 6th International Conference on Computer and Communications, ICCC 2020. Institute of Electrical and Electronics Engineers Inc. 2020. p. 1706-1710. 9345080. (2020 IEEE 6th International Conference on Computer and Communications, ICCC 2020). doi: 10.1109/ICCC51575.2020.9345080

Preprocessing of Massive Flight Data Based on Noise and Dimension Reduction

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