An Intelligent Fault Classification Method Based on Data-Driven Stability Margin

Xuejiao Wang; Hao Luo; Kuan Li; Shen Yin; Okyay Kaynak

doi:10.1109/IAI50351.2020.9262218

An Intelligent Fault Classification Method Based on Data-Driven Stability Margin

Xuejiao Wang
, Hao Luo
, Kuan Li
, Shen Yin
, Okyay Kaynak

School of Astronautics

Harbin Institute of Technology

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

Abstract

Thanks to rapid development of artificial intelligence (AI), a new branch of computer science, modern industry system becomes increasingly intelligent. What's more, mountains of data in industrial process can be saved for data-driven intelligent fault detection and classification. A method of intelligent data-driven fault classification based on stability margin is proposed in this paper, which gives a data-driven stability margin solution. As an important feature, the stability margin, together with the input and output (I/O) data, is input into the LM-BP neural network multi-classifier for fault classification. Moreover, the proposed method is demonstrated to be effective with high accuracy through a DC motor benchmark.

Original language	English
Title of host publication	2nd International Conference on Industrial Artificial Intelligence, IAI 2020
Publisher	Institute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)	9781728182162
DOIs	https://doi.org/10.1109/IAI50351.2020.9262218
State	Published - 23 Oct 2020
Event	2nd International Conference on Industrial Artificial Intelligence, IAI 2020 - Shenyang, China Duration: 23 Oct 2020 → 25 Oct 2020

Publication series

Name	2nd International Conference on Industrial Artificial Intelligence, IAI 2020

Conference

Conference	2nd International Conference on Industrial Artificial Intelligence, IAI 2020
Country/Territory	China
City	Shenyang
Period	23/10/20 → 25/10/20

Keywords

Data-driven stability margin
LM-BP neural network multi-classifier
real-time fault classification

Access to Document

10.1109/IAI50351.2020.9262218

Cite this

Wang, X., Luo, H., Li, K., Yin, S., & Kaynak, O. (2020). An Intelligent Fault Classification Method Based on Data-Driven Stability Margin. In 2nd International Conference on Industrial Artificial Intelligence, IAI 2020 Article 9262218 (2nd International Conference on Industrial Artificial Intelligence, IAI 2020). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/IAI50351.2020.9262218

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title = "An Intelligent Fault Classification Method Based on Data-Driven Stability Margin",

abstract = "Thanks to rapid development of artificial intelligence (AI), a new branch of computer science, modern industry system becomes increasingly intelligent. What's more, mountains of data in industrial process can be saved for data-driven intelligent fault detection and classification. A method of intelligent data-driven fault classification based on stability margin is proposed in this paper, which gives a data-driven stability margin solution. As an important feature, the stability margin, together with the input and output (I/O) data, is input into the LM-BP neural network multi-classifier for fault classification. Moreover, the proposed method is demonstrated to be effective with high accuracy through a DC motor benchmark.",

keywords = "Data-driven stability margin, LM-BP neural network multi-classifier, real-time fault classification",

author = "Xuejiao Wang and Hao Luo and Kuan Li and Shen Yin and Okyay Kaynak",

note = "Publisher Copyright: {\textcopyright} 2020 IEEE.; 2nd International Conference on Industrial Artificial Intelligence, IAI 2020 ; Conference date: 23-10-2020 Through 25-10-2020",

year = "2020",

month = oct,

day = "23",

doi = "10.1109/IAI50351.2020.9262218",

language = "英语",

series = "2nd International Conference on Industrial Artificial Intelligence, IAI 2020",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

booktitle = "2nd International Conference on Industrial Artificial Intelligence, IAI 2020",

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Wang, X, Luo, H, Li, K, Yin, S & Kaynak, O 2020, An Intelligent Fault Classification Method Based on Data-Driven Stability Margin. in 2nd International Conference on Industrial Artificial Intelligence, IAI 2020., 9262218, 2nd International Conference on Industrial Artificial Intelligence, IAI 2020, Institute of Electrical and Electronics Engineers Inc., 2nd International Conference on Industrial Artificial Intelligence, IAI 2020, Shenyang, China, 23/10/20. https://doi.org/10.1109/IAI50351.2020.9262218

An Intelligent Fault Classification Method Based on Data-Driven Stability Margin. / Wang, Xuejiao; Luo, Hao; Li, Kuan et al.
2nd International Conference on Industrial Artificial Intelligence, IAI 2020. Institute of Electrical and Electronics Engineers Inc., 2020. 9262218 (2nd International Conference on Industrial Artificial Intelligence, IAI 2020).

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

TY - GEN

T1 - An Intelligent Fault Classification Method Based on Data-Driven Stability Margin

AU - Wang, Xuejiao

AU - Luo, Hao

AU - Li, Kuan

AU - Yin, Shen

AU - Kaynak, Okyay

PY - 2020/10/23

Y1 - 2020/10/23

N2 - Thanks to rapid development of artificial intelligence (AI), a new branch of computer science, modern industry system becomes increasingly intelligent. What's more, mountains of data in industrial process can be saved for data-driven intelligent fault detection and classification. A method of intelligent data-driven fault classification based on stability margin is proposed in this paper, which gives a data-driven stability margin solution. As an important feature, the stability margin, together with the input and output (I/O) data, is input into the LM-BP neural network multi-classifier for fault classification. Moreover, the proposed method is demonstrated to be effective with high accuracy through a DC motor benchmark.

AB - Thanks to rapid development of artificial intelligence (AI), a new branch of computer science, modern industry system becomes increasingly intelligent. What's more, mountains of data in industrial process can be saved for data-driven intelligent fault detection and classification. A method of intelligent data-driven fault classification based on stability margin is proposed in this paper, which gives a data-driven stability margin solution. As an important feature, the stability margin, together with the input and output (I/O) data, is input into the LM-BP neural network multi-classifier for fault classification. Moreover, the proposed method is demonstrated to be effective with high accuracy through a DC motor benchmark.

KW - Data-driven stability margin

KW - LM-BP neural network multi-classifier

KW - real-time fault classification

UR - https://www.scopus.com/pages/publications/85098482081

U2 - 10.1109/IAI50351.2020.9262218

DO - 10.1109/IAI50351.2020.9262218

M3 - 会议稿件

AN - SCOPUS:85098482081

T3 - 2nd International Conference on Industrial Artificial Intelligence, IAI 2020

BT - 2nd International Conference on Industrial Artificial Intelligence, IAI 2020

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 2nd International Conference on Industrial Artificial Intelligence, IAI 2020

Y2 - 23 October 2020 through 25 October 2020

ER -

Wang X, Luo H, Li K, Yin S, Kaynak O. An Intelligent Fault Classification Method Based on Data-Driven Stability Margin. In 2nd International Conference on Industrial Artificial Intelligence, IAI 2020. Institute of Electrical and Electronics Engineers Inc. 2020. 9262218. (2nd International Conference on Industrial Artificial Intelligence, IAI 2020). doi: 10.1109/IAI50351.2020.9262218

An Intelligent Fault Classification Method Based on Data-Driven Stability Margin

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