Channel State Information (CSI) analysis for Predictive Maintenance using Convolutional Neural Network (CNN)

Prachi  Bagave; Jeroen Linssen; Wouter B. Teeuw; Jeroen  Klein Brinke; Nirvana  Meratnia

doi:10.1145/3359427.3361917

Channel State Information (CSI) analysis for Predictive Maintenance using Convolutional Neural Network (CNN)

Prachi Bagave^*, Jeroen Linssen, Wouter B. Teeuw, Jeroen Klein Brinke, Nirvana Meratnia

^*Corresponding author for this work

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

6 Citations (Scopus)

68 Downloads (Pure)

Abstract

With the onset of the fourth industrial revolution, predictive maintenance using wireless sensing technologies has been in high demand. This motivates to investigate the potential of WiFi CSI as a sensor for understanding the operation of machines. Since rotating motors are one of the fundamental elements in many complex machines, this paper focuses on the classification of CSI signals influenced by rotating motors at different speeds. As WiFi CSI technology is still not mature, we focus on data collection and study the sensitivity and reliability of data for this type of applications. We observe that CNNs are suitable to classify the speeds of motors and is also sensitive to speeds close to each other when operated in ideal network condition. However, in practical network conditions, unreliability of the data and the inability of CNN to classify it remains a challenge.

Original language	English
Title of host publication	DATA'19
Subtitle of host publication	Proceedings of the 2nd Workshop on Data Acquisition To Analysis
Place of Publication	New York, NY
Pages	51-56
Number of pages	6
ISBN (Electronic)	978-1-4503-6993-0
DOIs	https://doi.org/10.1145/3359427.3361917
Publication status	Published - 10 Nov 2019
Event	2nd Workshop on Data Acquisition To Analysis, DATA 2019 - Columbus University, New York, United States Duration: 10 Nov 2019 → 10 Nov 2019 Conference number: 2

Conference

Conference	2nd Workshop on Data Acquisition To Analysis, DATA 2019
Abbreviated title	DATA
Country/Territory	United States
City	New York
Period	10/11/19 → 10/11/19

Keywords

2024 OA procedure

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

Access to Document

10.1145/3359427.3361917

ArticleFinal published version, 8.68 MBLicence: Taverne

Cite this

@inproceedings{5a8c18c4574140c48fd34382df831e31,

title = "Channel State Information (CSI) analysis for Predictive Maintenance using Convolutional Neural Network (CNN)",

abstract = "With the onset of the fourth industrial revolution, predictive maintenance using wireless sensing technologies has been in high demand. This motivates to investigate the potential of WiFi CSI as a sensor for understanding the operation of machines. Since rotating motors are one of the fundamental elements in many complex machines, this paper focuses on the classification of CSI signals influenced by rotating motors at different speeds. As WiFi CSI technology is still not mature, we focus on data collection and study the sensitivity and reliability of data for this type of applications. We observe that CNNs are suitable to classify the speeds of motors and is also sensitive to speeds close to each other when operated in ideal network condition. However, in practical network conditions, unreliability of the data and the inability of CNN to classify it remains a challenge.",

keywords = "2024 OA procedure",

author = "Prachi Bagave and Jeroen Linssen and Teeuw, {Wouter B.} and {Klein Brinke}, Jeroen and Nirvana Meratnia",

year = "2019",

month = nov,

day = "10",

doi = "10.1145/3359427.3361917",

language = "English",

pages = "51--56",

booktitle = "DATA'19",

note = "2nd Workshop on Data Acquisition To Analysis, DATA 2019, DATA ; Conference date: 10-11-2019 Through 10-11-2019",

}

Bagave, P, Linssen, J, Teeuw, WB, Klein Brinke, J & Meratnia, N 2019, Channel State Information (CSI) analysis for Predictive Maintenance using Convolutional Neural Network (CNN). in DATA'19: Proceedings of the 2nd Workshop on Data Acquisition To Analysis. New York, NY, pp. 51-56, 2nd Workshop on Data Acquisition To Analysis, DATA 2019, New York, New York, United States, 10/11/19. https://doi.org/10.1145/3359427.3361917

Channel State Information (CSI) analysis for Predictive Maintenance using Convolutional Neural Network (CNN). / Bagave, Prachi ; Linssen, Jeroen; Teeuw, Wouter B. et al.
DATA'19: Proceedings of the 2nd Workshop on Data Acquisition To Analysis. New York, NY, 2019. p. 51-56.

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

TY - GEN

T1 - Channel State Information (CSI) analysis for Predictive Maintenance using Convolutional Neural Network (CNN)

AU - Bagave, Prachi

AU - Linssen, Jeroen

AU - Teeuw, Wouter B.

AU - Klein Brinke, Jeroen

AU - Meratnia, Nirvana

N1 - Conference code: 2

PY - 2019/11/10

Y1 - 2019/11/10

N2 - With the onset of the fourth industrial revolution, predictive maintenance using wireless sensing technologies has been in high demand. This motivates to investigate the potential of WiFi CSI as a sensor for understanding the operation of machines. Since rotating motors are one of the fundamental elements in many complex machines, this paper focuses on the classification of CSI signals influenced by rotating motors at different speeds. As WiFi CSI technology is still not mature, we focus on data collection and study the sensitivity and reliability of data for this type of applications. We observe that CNNs are suitable to classify the speeds of motors and is also sensitive to speeds close to each other when operated in ideal network condition. However, in practical network conditions, unreliability of the data and the inability of CNN to classify it remains a challenge.

AB - With the onset of the fourth industrial revolution, predictive maintenance using wireless sensing technologies has been in high demand. This motivates to investigate the potential of WiFi CSI as a sensor for understanding the operation of machines. Since rotating motors are one of the fundamental elements in many complex machines, this paper focuses on the classification of CSI signals influenced by rotating motors at different speeds. As WiFi CSI technology is still not mature, we focus on data collection and study the sensitivity and reliability of data for this type of applications. We observe that CNNs are suitable to classify the speeds of motors and is also sensitive to speeds close to each other when operated in ideal network condition. However, in practical network conditions, unreliability of the data and the inability of CNN to classify it remains a challenge.

KW - 2024 OA procedure

U2 - 10.1145/3359427.3361917

DO - 10.1145/3359427.3361917

M3 - Conference contribution

SP - 51

EP - 56

BT - DATA'19

CY - New York, NY

T2 - 2nd Workshop on Data Acquisition To Analysis, DATA 2019

Y2 - 10 November 2019 through 10 November 2019

ER -

Channel State Information (CSI) analysis for Predictive Maintenance using Convolutional Neural Network (CNN)

Abstract

Conference

Keywords

UN SDGs

Access to Document

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Cite this