A state and fault prediction method based on RBF neural networks

Yong Tao; Jiaqi Zheng; Tianmiao Wang; Yaoguang Hu

doi:10.1109/ARSO.2016.7736285

A state and fault prediction method based on RBF neural networks

Yong Tao^*, Jiaqi Zheng, Tianmiao Wang, Yaoguang Hu

^*Corresponding author for this work

Beihang University

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

8 Citations (Scopus)

Abstract

A state and fault prediction method based on RBF neural networks is proposed. The agricultural machinery is chosen as the experimental object of the method. There are 4 health level, such as failure, hazardous, sub-healthy and healthy. Some data of different provinces have been obtained, the health level can be acquired by RBF neural networks. The mathematical model of agricultural machinery is difficult to be proposed in this paper, so the traditional control algorithm can't be used in agricultural machinery. However, the RBF neural networks can solve this problem. At the same time, some vital factors should be considered, such as mileages, rotational speed, stubble height, water temperature, oil pressure of agricultural machinery. The rotational speed and stubble height have a big effect on fault prediction of agriculture. The experimental results verify the effectiveness of the proposed method.

Original language	English
Title of host publication	2016 IEEE Workshop on Advanced Robotics and its Social Impacts, IEEE ARSO 2016
Publisher	IEEE Computer Society
Pages	221-225
Number of pages	5
ISBN (Electronic)	9781509040773
DOIs	https://doi.org/10.1109/ARSO.2016.7736285
Publication status	Published - 4 Nov 2016
Externally published	Yes
Event	2016 IEEE Workshop on Advanced Robotics and its Social Impacts, IEEE ARSO 2016 - Shanghai, China Duration: 7 Jul 2016 → 10 Jul 2016

Publication series

Name	Proceedings of IEEE Workshop on Advanced Robotics and its Social Impacts, ARSO
Volume	2016-November
ISSN (Print)	2162-7568
ISSN (Electronic)	2162-7576

Conference

Conference	2016 IEEE Workshop on Advanced Robotics and its Social Impacts, IEEE ARSO 2016
Country/Territory	China
City	Shanghai
Period	7/07/16 → 10/07/16

Access to Document

10.1109/ARSO.2016.7736285

Cite this

Tao, Y., Zheng, J., Wang, T., & Hu, Y. (2016). A state and fault prediction method based on RBF neural networks. In 2016 IEEE Workshop on Advanced Robotics and its Social Impacts, IEEE ARSO 2016 (pp. 221-225). Article 7736285 (Proceedings of IEEE Workshop on Advanced Robotics and its Social Impacts, ARSO; Vol. 2016-November). IEEE Computer Society. https://doi.org/10.1109/ARSO.2016.7736285

@inproceedings{7d2d0d02986c415f8997c99a9486026a,

title = "A state and fault prediction method based on RBF neural networks",

abstract = "A state and fault prediction method based on RBF neural networks is proposed. The agricultural machinery is chosen as the experimental object of the method. There are 4 health level, such as failure, hazardous, sub-healthy and healthy. Some data of different provinces have been obtained, the health level can be acquired by RBF neural networks. The mathematical model of agricultural machinery is difficult to be proposed in this paper, so the traditional control algorithm can't be used in agricultural machinery. However, the RBF neural networks can solve this problem. At the same time, some vital factors should be considered, such as mileages, rotational speed, stubble height, water temperature, oil pressure of agricultural machinery. The rotational speed and stubble height have a big effect on fault prediction of agriculture. The experimental results verify the effectiveness of the proposed method.",

author = "Yong Tao and Jiaqi Zheng and Tianmiao Wang and Yaoguang Hu",

note = "Publisher Copyright: {\textcopyright} 2016 IEEE.; 2016 IEEE Workshop on Advanced Robotics and its Social Impacts, IEEE ARSO 2016 ; Conference date: 07-07-2016 Through 10-07-2016",

year = "2016",

month = nov,

day = "4",

doi = "10.1109/ARSO.2016.7736285",

language = "English",

series = "Proceedings of IEEE Workshop on Advanced Robotics and its Social Impacts, ARSO",

publisher = "IEEE Computer Society",

pages = "221--225",

booktitle = "2016 IEEE Workshop on Advanced Robotics and its Social Impacts, IEEE ARSO 2016",

address = "United States",

}

Tao, Y, Zheng, J, Wang, T & Hu, Y 2016, A state and fault prediction method based on RBF neural networks. in 2016 IEEE Workshop on Advanced Robotics and its Social Impacts, IEEE ARSO 2016., 7736285, Proceedings of IEEE Workshop on Advanced Robotics and its Social Impacts, ARSO, vol. 2016-November, IEEE Computer Society, pp. 221-225, 2016 IEEE Workshop on Advanced Robotics and its Social Impacts, IEEE ARSO 2016, Shanghai, China, 7/07/16. https://doi.org/10.1109/ARSO.2016.7736285

A state and fault prediction method based on RBF neural networks. / Tao, Yong; Zheng, Jiaqi; Wang, Tianmiao et al.
2016 IEEE Workshop on Advanced Robotics and its Social Impacts, IEEE ARSO 2016. IEEE Computer Society, 2016. p. 221-225 7736285 (Proceedings of IEEE Workshop on Advanced Robotics and its Social Impacts, ARSO; Vol. 2016-November).

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

TY - GEN

T1 - A state and fault prediction method based on RBF neural networks

AU - Tao, Yong

AU - Zheng, Jiaqi

AU - Wang, Tianmiao

AU - Hu, Yaoguang

PY - 2016/11/4

Y1 - 2016/11/4

N2 - A state and fault prediction method based on RBF neural networks is proposed. The agricultural machinery is chosen as the experimental object of the method. There are 4 health level, such as failure, hazardous, sub-healthy and healthy. Some data of different provinces have been obtained, the health level can be acquired by RBF neural networks. The mathematical model of agricultural machinery is difficult to be proposed in this paper, so the traditional control algorithm can't be used in agricultural machinery. However, the RBF neural networks can solve this problem. At the same time, some vital factors should be considered, such as mileages, rotational speed, stubble height, water temperature, oil pressure of agricultural machinery. The rotational speed and stubble height have a big effect on fault prediction of agriculture. The experimental results verify the effectiveness of the proposed method.

AB - A state and fault prediction method based on RBF neural networks is proposed. The agricultural machinery is chosen as the experimental object of the method. There are 4 health level, such as failure, hazardous, sub-healthy and healthy. Some data of different provinces have been obtained, the health level can be acquired by RBF neural networks. The mathematical model of agricultural machinery is difficult to be proposed in this paper, so the traditional control algorithm can't be used in agricultural machinery. However, the RBF neural networks can solve this problem. At the same time, some vital factors should be considered, such as mileages, rotational speed, stubble height, water temperature, oil pressure of agricultural machinery. The rotational speed and stubble height have a big effect on fault prediction of agriculture. The experimental results verify the effectiveness of the proposed method.

UR - http://www.scopus.com/inward/record.url?scp=85006999641&partnerID=8YFLogxK

U2 - 10.1109/ARSO.2016.7736285

DO - 10.1109/ARSO.2016.7736285

M3 - Conference contribution

AN - SCOPUS:85006999641

T3 - Proceedings of IEEE Workshop on Advanced Robotics and its Social Impacts, ARSO

SP - 221

EP - 225

BT - 2016 IEEE Workshop on Advanced Robotics and its Social Impacts, IEEE ARSO 2016

PB - IEEE Computer Society

T2 - 2016 IEEE Workshop on Advanced Robotics and its Social Impacts, IEEE ARSO 2016

Y2 - 7 July 2016 through 10 July 2016

ER -

A state and fault prediction method based on RBF neural networks

Abstract

Publication series

Conference

Access to Document

Other files and links

Fingerprint

Cite this