Data fusion of wireless sensor network for prognosis and diagnosis of mechanical systems

Qinyin Chen; Yanting Hu; Jingbo Xia; Zhe Chen; Hsien Wei Tseng

doi:10.1109/ICICE.2017.8479136

Data fusion of wireless sensor network for prognosis and diagnosis of mechanical systems

Qinyin Chen, Yanting Hu, Jingbo Xia^*, Zhe Chen, Hsien Wei Tseng

^*Kontaktforfatter

Publikation: Bidrag til bog/antologi/rapport/konference proceeding › Konferenceartikel i proceeding › Forskning › peer review

3 Citationer (Scopus)

Abstract

In order to improve the data collection efficiency, network lifetime and reduce transmission congestion of a Wireless Sensor Network, a data fusion algorithm based on the combination of BP Neural Network (NN) and Wavelet Packet is proposed. The research is based on Industrial Wireless Sensor Networks (IWSNs) application on mechanical diagnosis of wind turbine monitoring. This proposed algorithm uses the cluster protocol, and each cluster head is modeled with a three layers NN. The raw data feature is extracted by the Wavelet Packet and transmitted to the sink node for feature fusion. The fault classification result is used for mechanical diagnosis; the simulation results show that the diagnosis precision can achieve 90%.

Originalsprog	Engelsk
Titel	Proceedings of the 2017 IEEE International Conference on Information, Communication and Engineering : Information and Innovation for Modern Technology, ICICE 2017
Redaktører	Artde Donald Kin-Tak Lam, Stephen D. Prior, Teen-Hang Meen
Antal sider	4
Forlag	IEEE Press
Publikationsdato	nov. 2017
Sider	331-334
Artikelnummer	8479136
ISBN (Trykt)	978-1-5386-3203-1
ISBN (Elektronisk)	978-1-5386-3202-4
DOI	https://doi.org/10.1109/ICICE.2017.8479136
Status	Udgivet - nov. 2017
Begivenhed	2017 IEEE International Conference on Information, Communication and Engineering, ICICE 2017 - Xiamen, Fujian, Kina Varighed: 17 nov. 2017 → 20 nov. 2017

Konference

Konference	2017 IEEE International Conference on Information, Communication and Engineering, ICICE 2017
Land/Område	Kina
By	Xiamen, Fujian
Periode	17/11/2017 → 20/11/2017
Sponsor	Fujian Information Industry Association, Fuzhou Cross-Strait Industrial Design Creative Park, Kaoke ( Fujian ) Industrial Design Co., Ltd.

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10.1109/ICICE.2017.8479136

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Citationsformater

Chen, Q., Hu, Y., Xia, J., Chen, Z., & Tseng, H. W. (2017). Data fusion of wireless sensor network for prognosis and diagnosis of mechanical systems. I A. D. K-T. Lam, S. D. Prior, & T-H. Meen (red.), Proceedings of the 2017 IEEE International Conference on Information, Communication and Engineering: Information and Innovation for Modern Technology, ICICE 2017 (s. 331-334). Artikel 8479136 IEEE Press. https://doi.org/10.1109/ICICE.2017.8479136

Chen, Qinyin ; Hu, Yanting ; Xia, Jingbo et al. / Data fusion of wireless sensor network for prognosis and diagnosis of mechanical systems. Proceedings of the 2017 IEEE International Conference on Information, Communication and Engineering: Information and Innovation for Modern Technology, ICICE 2017. red. / Artde Donald Kin-Tak Lam ; Stephen D. Prior ; Teen-Hang Meen. IEEE Press, 2017. s. 331-334

@inproceedings{0357b4b0aa60428980988c8902844fab,

title = "Data fusion of wireless sensor network for prognosis and diagnosis of mechanical systems",

abstract = "In order to improve the data collection efficiency, network lifetime and reduce transmission congestion of a Wireless Sensor Network, a data fusion algorithm based on the combination of BP Neural Network (NN) and Wavelet Packet is proposed. The research is based on Industrial Wireless Sensor Networks (IWSNs) application on mechanical diagnosis of wind turbine monitoring. This proposed algorithm uses the cluster protocol, and each cluster head is modeled with a three layers NN. The raw data feature is extracted by the Wavelet Packet and transmitted to the sink node for feature fusion. The fault classification result is used for mechanical diagnosis; the simulation results show that the diagnosis precision can achieve 90%.",

keywords = "Data Fusion, Neural Network, Wireless Sensor Network",

author = "Qinyin Chen and Yanting Hu and Jingbo Xia and Zhe Chen and Tseng, {Hsien Wei}",

year = "2017",

month = nov,

doi = "10.1109/ICICE.2017.8479136",

language = "English",

isbn = "978-1-5386-3203-1",

pages = "331--334",

editor = "Lam, {Artde Donald Kin-Tak} and Prior, {Stephen D.} and Teen-Hang Meen",

booktitle = "Proceedings of the 2017 IEEE International Conference on Information, Communication and Engineering",

publisher = "IEEE Press",

note = "2017 IEEE International Conference on Information, Communication and Engineering, ICICE 2017 ; Conference date: 17-11-2017 Through 20-11-2017",

}

Chen, Q, Hu, Y, Xia, J, Chen, Z & Tseng, HW 2017, Data fusion of wireless sensor network for prognosis and diagnosis of mechanical systems. i ADK-T Lam, SD Prior & T-H Meen (red), Proceedings of the 2017 IEEE International Conference on Information, Communication and Engineering: Information and Innovation for Modern Technology, ICICE 2017., 8479136, IEEE Press, s. 331-334, 2017 IEEE International Conference on Information, Communication and Engineering, ICICE 2017, Xiamen, Fujian, Kina, 17/11/2017. https://doi.org/10.1109/ICICE.2017.8479136

Data fusion of wireless sensor network for prognosis and diagnosis of mechanical systems. / Chen, Qinyin; Hu, Yanting; Xia, Jingbo et al.
Proceedings of the 2017 IEEE International Conference on Information, Communication and Engineering: Information and Innovation for Modern Technology, ICICE 2017. red. / Artde Donald Kin-Tak Lam; Stephen D. Prior; Teen-Hang Meen. IEEE Press, 2017. s. 331-334 8479136.

Publikation: Bidrag til bog/antologi/rapport/konference proceeding › Konferenceartikel i proceeding › Forskning › peer review

TY - GEN

T1 - Data fusion of wireless sensor network for prognosis and diagnosis of mechanical systems

AU - Chen, Qinyin

AU - Hu, Yanting

AU - Xia, Jingbo

AU - Chen, Zhe

AU - Tseng, Hsien Wei

PY - 2017/11

Y1 - 2017/11

N2 - In order to improve the data collection efficiency, network lifetime and reduce transmission congestion of a Wireless Sensor Network, a data fusion algorithm based on the combination of BP Neural Network (NN) and Wavelet Packet is proposed. The research is based on Industrial Wireless Sensor Networks (IWSNs) application on mechanical diagnosis of wind turbine monitoring. This proposed algorithm uses the cluster protocol, and each cluster head is modeled with a three layers NN. The raw data feature is extracted by the Wavelet Packet and transmitted to the sink node for feature fusion. The fault classification result is used for mechanical diagnosis; the simulation results show that the diagnosis precision can achieve 90%.

AB - In order to improve the data collection efficiency, network lifetime and reduce transmission congestion of a Wireless Sensor Network, a data fusion algorithm based on the combination of BP Neural Network (NN) and Wavelet Packet is proposed. The research is based on Industrial Wireless Sensor Networks (IWSNs) application on mechanical diagnosis of wind turbine monitoring. This proposed algorithm uses the cluster protocol, and each cluster head is modeled with a three layers NN. The raw data feature is extracted by the Wavelet Packet and transmitted to the sink node for feature fusion. The fault classification result is used for mechanical diagnosis; the simulation results show that the diagnosis precision can achieve 90%.

KW - Data Fusion

KW - Neural Network

KW - Wireless Sensor Network

UR - http://www.scopus.com/inward/record.url?scp=85056252998&partnerID=8YFLogxK

U2 - 10.1109/ICICE.2017.8479136

DO - 10.1109/ICICE.2017.8479136

M3 - Article in proceeding

AN - SCOPUS:85056252998

SN - 978-1-5386-3203-1

SP - 331

EP - 334

BT - Proceedings of the 2017 IEEE International Conference on Information, Communication and Engineering

A2 - Lam, Artde Donald Kin-Tak

A2 - Prior, Stephen D.

A2 - Meen, Teen-Hang

PB - IEEE Press

T2 - 2017 IEEE International Conference on Information, Communication and Engineering, ICICE 2017

Y2 - 17 November 2017 through 20 November 2017

ER -

Chen Q, Hu Y, Xia J, Chen Z, Tseng HW. Data fusion of wireless sensor network for prognosis and diagnosis of mechanical systems. I Lam ADK-T, Prior SD, Meen T-H, red., Proceedings of the 2017 IEEE International Conference on Information, Communication and Engineering: Information and Innovation for Modern Technology, ICICE 2017. IEEE Press. 2017. s. 331-334. 8479136 doi: 10.1109/ICICE.2017.8479136

Data fusion of wireless sensor network for prognosis and diagnosis of mechanical systems

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